{"id":3050,"date":"2026-09-24T09:00:00","date_gmt":"2026-09-24T09:00:00","guid":{"rendered":"https:\/\/otus.ru\/journal\/?p=3050"},"modified":"2026-09-25T15:30:00","modified_gmt":"2026-09-25T15:30:00","slug":"piton-i-mashinnoe-obuchenie-chto-pomozhet-razrabotchiku","status":"publish","type":"post","link":"https:\/\/otus.ru\/journal\/piton-i-mashinnoe-obuchenie-chto-pomozhet-razrabotchiku\/","title":{"rendered":"Python \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f: \u043f\u043e\u0447\u0435\u043c\u0443 \u0438\u043c\u0435\u043d\u043d\u043e \u043e\u043d, \u043a\u0430\u043a\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u043d\u0443\u0436\u043d\u044b \u0438 \u0441 \u0447\u0435\u0433\u043e \u043d\u0430\u0447\u0430\u0442\u044c"},"content":{"rendered":"<p><strong>Python \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f<\/strong> &#8212; \u044d\u0442\u043e \u044f\u0437\u044b\u043a, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043e\u043f\u0438\u0441\u044b\u0432\u0430\u044e\u0442 \u0432\u0435\u0441\u044c \u043f\u0443\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438: \u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0443 \u0438 \u043e\u0447\u0438\u0441\u0442\u043a\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435, \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0443 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0438 \u0437\u0430\u043f\u0443\u0441\u043a. \u041f\u0440\u0438 \u044d\u0442\u043e\u043c \u0442\u044f\u0436\u0435\u043b\u044b\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0430\u0435\u0442 \u043d\u0435 \u0441\u0430\u043c Python, \u0430 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 C, C++, Fortran \u0438 CUDA: Python \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u0438\u043c\u0438 \u043a\u0430\u043a \u0443\u0434\u043e\u0431\u043d\u044b\u0439 \u00ab\u043f\u0443\u043b\u044c\u0442\u00bb. \u041f\u043e\u044d\u0442\u043e\u043c\u0443 ML \u043d\u0430 Python &#8212; \u044d\u0442\u043e \u0432\u0441\u0435\u0433\u0434\u0430 \u0441\u0432\u044f\u0437\u043a\u0430 \u00ab\u044f\u0437\u044b\u043a + \u0441\u0442\u0435\u043a \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u00bb, \u0430 \u043d\u0435 \u043e\u0434\u0438\u043d \u044f\u0437\u044b\u043a.<\/p>\n<div class=\"oj-toc\" style=\"background:#f6f6f6;border-radius:8px;padding:14px 20px;margin:20px 0\">\n<p><strong>\u0421\u043e\u0434\u0435\u0440\u0436\u0430\u043d\u0438\u0435<\/strong><\/p>\n<ol>\n<li><a href=\"#s1\">\u0427\u0435\u0442\u044b\u0440\u0435 \u0442\u0435\u0440\u043c\u0438\u043d\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u0443\u0442\u0430\u044e\u0442<\/a><\/li>\n<li><a href=\"#s2\">\u041f\u043e\u0447\u0435\u043c\u0443 \u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 Python<\/a><\/li>\n<li><a href=\"#s3\">\u0421\u0442\u0435\u043a \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a: \u0447\u0442\u043e \u0437\u0430 \u0447\u0442\u043e \u043e\u0442\u0432\u0435\u0447\u0430\u0435\u0442<\/a><\/li>\n<li><a href=\"#s4\">\u041f\u0435\u0440\u0432\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c: \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u043f\u043e\u043b\u043d\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440<\/a><\/li>\n<li><a href=\"#s5\">\u0422\u0438\u043f\u0438\u0447\u043d\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430: \u0443\u0442\u0435\u0447\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445<\/a><\/li>\n<li><a href=\"#s6\">\u0421 \u0447\u0435\u0433\u043e \u043d\u0430\u0447\u0430\u0442\u044c: \u0434\u043e\u0440\u043e\u0436\u043d\u0430\u044f \u043a\u0430\u0440\u0442\u0430 \u0434\u043e \u043f\u0435\u0440\u0432\u043e\u0433\u043e \u043f\u0440\u043e\u0435\u043a\u0442\u0430<\/a><\/li>\n<li><a href=\"#s7\">\u0412\u044b\u0432\u043e\u0434\u044b<\/a><\/li>\n<li><a href=\"#s8\">\u0413\u0434\u0435 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \/ \u0441\u0432\u044f\u0437\u044c \u0441 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u043e\u0439<\/a><\/li>\n<li><a href=\"#s9\">FAQ<\/a><\/li>\n<\/ol>\n<\/div>\n<p>\u041d\u0438\u0436\u0435 &#8212; \u043f\u043e\u0447\u0435\u043c\u0443 \u0432 ML \u043f\u043e\u0431\u0435\u0434\u0438\u043b \u0438\u043c\u0435\u043d\u043d\u043e Python \u0438 \u0433\u0434\u0435 \u0443 \u043d\u0435\u0433\u043e \u0433\u0440\u0430\u043d\u0438\u0446\u044b, \u043a\u0430\u043a\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0437\u0430 \u0447\u0442\u043e \u043e\u0442\u0432\u0435\u0447\u0430\u044e\u0442, \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u0430\u044f \u0440\u0430\u0431\u043e\u0447\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0441 \u0440\u0430\u0437\u0431\u043e\u0440\u043e\u043c \u0438 \u0434\u043e\u0440\u043e\u0436\u043d\u0430\u044f \u043a\u0430\u0440\u0442\u0430 \u0434\u043b\u044f \u0441\u0442\u0430\u0440\u0442\u0430. \u041f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u0440\u043e\u0432\u0435\u0440\u0435\u043d\u044b 24.09.2026 \u043d\u0430 Python 3.14.7, NumPy 2.5.3, pandas 3.0.6 \u0438 scikit-learn 1.9.1.<\/p>\n<h2 id=\"s1\">\u0427\u0435\u0442\u044b\u0440\u0435 \u0442\u0435\u0440\u043c\u0438\u043d\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u0443\u0442\u0430\u044e\u0442<\/h2>\n<ul>\n<li><strong>\u042f\u0437\u044b\u043a<\/strong> &#8212; Python \u0441\u0430\u043c \u043f\u043e \u0441\u0435\u0431\u0435: \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0442\u0438\u043f\u044b, \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0430\u044f \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430. \u041c\u043e\u0434\u0435\u043b\u0435\u0439 \u0432 \u043d\u0435\u043c \u043d\u0435\u0442.<\/li>\n<li><strong>\u0411\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430<\/strong> &#8212; \u0433\u043e\u0442\u043e\u0432\u044b\u0439 \u043a\u043e\u0434, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u0432\u044b\u0437\u044b\u0432\u0430\u0435\u0442\u0435 \u0441\u0430\u043c\u0438: <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">numpy.mean()<\/code>, <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">pandas.read_csv()<\/code>.<\/li>\n<li><strong>\u0424\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a<\/strong> &#8212; \u043a\u0430\u0440\u043a\u0430\u0441, \u0432 \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u0432\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0435\u0442\u0435 \u0441\u0432\u043e\u0439 \u043a\u043e\u0434, \u0430 \u043e\u043d \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u043c: PyTorch \u0441\u0430\u043c \u0441\u0447\u0438\u0442\u0430\u0435\u0442 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u044b \u0438 \u043f\u0440\u043e\u0433\u043e\u043d\u044f\u0435\u0442 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 GPU. \u0413\u0440\u0430\u043d\u0438\u0446\u0430 \u0443\u0441\u043b\u043e\u0432\u043d\u0430\u044f: scikit-learn \u043d\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u043e\u0439, \u0438 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u043e\u043c.<\/li>\n<li><strong>\u041c\u043e\u0434\u0435\u043b\u044c<\/strong> &#8212; \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u044b\u0439 \u043e\u0431\u044a\u0435\u043a\u0442 \u0441 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430\u043c\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442 \u0434\u0430\u043d\u043d\u044b\u0435 \u0438 \u0432\u044b\u0434\u0430\u0435\u0442 \u043f\u0440\u043e\u0433\u043d\u043e\u0437. \u0415\u0435 \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430, \u043d\u043e \u043c\u043e\u0434\u0435\u043b\u044c &#8212; \u044d\u0442\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0430 \u043d\u0435 \u043a\u043e\u0434 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438.<\/li>\n<\/ul>\n<h2 id=\"s2\">\u041f\u043e\u0447\u0435\u043c\u0443 \u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 Python<\/h2>\n<p>\u041f\u0440\u0438\u0447\u0438\u043d\u0430 \u043d\u0435 \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e Python \u00ab\u0431\u044b\u0441\u0442\u0440\u044b\u0439\u00bb: \u0447\u0438\u0441\u0442\u044b\u0439 Python \u043a\u0430\u043a \u0440\u0430\u0437 \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u044b\u0439. \u0420\u0435\u0448\u0430\u044e\u0442 \u0442\u0440\u0438 \u0432\u0435\u0449\u0438.<\/p>\n<p>\u041f\u0435\u0440\u0432\u043e\u0435 &#8212; \u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u0430. \u0414\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0439 \u0437\u0430\u0434\u0430\u0447\u0438 ML \u0435\u0441\u0442\u044c \u0437\u0440\u0435\u043b\u0430\u044f \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0441 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0435\u0439, \u0438 \u0432\u0441\u0435 \u043e\u043d\u0438 \u043e\u0431\u043c\u0435\u043d\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u043e\u0434\u043d\u0438\u043c\u0438 \u0438 \u0442\u0435\u043c\u0438 \u0436\u0435 \u043c\u0430\u0441\u0441\u0438\u0432\u0430\u043c\u0438 NumPy. \u0412\u0442\u043e\u0440\u043e\u0435 &#8212; Python \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043a\u0430\u043a \u00ab\u043a\u043b\u0435\u0439\u00bb: \u0446\u0438\u043a\u043b \u043d\u0430 \u043c\u0438\u043b\u043b\u0438\u043e\u043d \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432 \u0443\u0445\u043e\u0434\u0438\u0442 \u0432\u043d\u0443\u0442\u0440\u044c NumPy \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f \u0441\u043a\u043e\u043c\u043f\u0438\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u043c \u043a\u043e\u0434\u043e\u043c. \u0422\u0440\u0435\u0442\u044c\u0435 &#8212; \u0438\u043d\u0442\u0435\u0440\u0430\u043a\u0442\u0438\u0432\u043d\u0430\u044f \u0440\u0430\u0431\u043e\u0442\u0430 \u0432 Jupyter: \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u043b \u0434\u0430\u043d\u043d\u044b\u0435, \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u043b \u0433\u0440\u0430\u0444\u0438\u043a, \u043f\u043e\u043f\u0440\u0430\u0432\u0438\u043b \u043f\u0440\u0438\u0437\u043d\u0430\u043a, \u043d\u0435 \u043f\u0435\u0440\u0435\u0437\u0430\u043f\u0443\u0441\u043a\u0430\u044f \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0443.<\/p>\n<p>\u0420\u0430\u0437\u043d\u0438\u0446\u0443 \u043c\u0435\u0436\u0434\u0443 \u0446\u0438\u043a\u043b\u043e\u043c Python \u0438 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u043c \u0432\u044b\u0437\u043e\u0432\u043e\u043c NumPy \u0432\u0438\u0434\u043d\u043e \u043d\u0430 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0441\u0443\u043c\u043c\u0435 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043e\u0432:<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\"><span style=\"color: #C678DD\">import<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">time<\/span>\n<span style=\"color: #C678DD\">import<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">numpy<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">as<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">np<\/span>\n\n<span style=\"color: #E06C75\">values<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E5C07B\">list<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E5C07B\">range<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">1_000_000<\/span><span style=\"color: #ABB2BF\">))<\/span>\n<span style=\"color: #E06C75\">arr<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">np<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">arange<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">1_000_000<\/span><span style=\"color: #ABB2BF\">)<\/span>\n\n<span style=\"color: #E06C75\">start<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">time<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">perf_counter<\/span><span style=\"color: #ABB2BF\">()<\/span>\n<span style=\"color: #E06C75\">total_py<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E5C07B\">sum<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">v<\/span> <span style=\"color: #56B6C2\">*<\/span> <span style=\"color: #E06C75\">v<\/span> <span style=\"color: #C678DD\">for<\/span> <span style=\"color: #E06C75\">v<\/span> <span style=\"color: #56B6C2\">in<\/span> <span style=\"color: #E06C75\">values<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">t_py<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">time<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">perf_counter<\/span><span style=\"color: #ABB2BF\">()<\/span> <span style=\"color: #56B6C2\">-<\/span> <span style=\"color: #E06C75\">start<\/span>\n\n<span style=\"color: #E06C75\">start<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">time<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">perf_counter<\/span><span style=\"color: #ABB2BF\">()<\/span>\n<span style=\"color: #E06C75\">total_np<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E5C07B\">int<\/span><span style=\"color: #ABB2BF\">((<\/span><span style=\"color: #E06C75\">arr<\/span> <span style=\"color: #56B6C2\">*<\/span> <span style=\"color: #E06C75\">arr<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">sum<\/span><span style=\"color: #ABB2BF\">())<\/span>\n<span style=\"color: #E06C75\">t_np<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">time<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">perf_counter<\/span><span style=\"color: #ABB2BF\">()<\/span> <span style=\"color: #56B6C2\">-<\/span> <span style=\"color: #E06C75\">start<\/span>\n\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">total_py<\/span> <span style=\"color: #56B6C2\">==<\/span> <span style=\"color: #E06C75\">total_np<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">total_np<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">f\"\u0447\u0438\u0441\u0442\u044b\u0439 Python: {<\/span><span style=\"color: #E06C75\">t_py<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #56B6C2\">*<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #D19A66\">1000<\/span><span style=\"color: #98C379\">:.1f} \u043c\u0441\"<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">f\"NumPy:         {<\/span><span style=\"color: #E06C75\">t_np<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #56B6C2\">*<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #D19A66\">1000<\/span><span style=\"color: #98C379\">:.1f} \u043c\u0441\"<\/span><span style=\"color: #ABB2BF\">)<\/span><\/code><\/pre>\n<p>\u041d\u0430 \u043c\u043e\u0435\u043c \u043f\u0440\u043e\u0433\u043e\u043d\u0435 (Docker, Python 3.14.7) \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0441\u043e\u0432\u043f\u0430\u043b, \u0430 \u0432\u0440\u0435\u043c\u044f \u043e\u0442\u043b\u0438\u0447\u0430\u043b\u043e\u0441\u044c \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0432 20 \u0440\u0430\u0437:<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\">True 333332833333500000\n\u0447\u0438\u0441\u0442\u044b\u0439 Python: 22.9 \u043c\u0441\nNumPy:         1.2 \u043c\u0441\n<\/code><\/pre>\n<p>\u041c\u0438\u043b\u043b\u0438\u0441\u0435\u043a\u0443\u043d\u0434\u044b \u0437\u0430\u0432\u0438\u0441\u044f\u0442 \u043e\u0442 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430 \u0438 \u043d\u0430\u0433\u0440\u0443\u0437\u043a\u0438, \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u0438\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u043f\u043e\u0440\u044f\u0434\u043e\u043a. \u041d\u043e \u0443 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0435\u0441\u0442\u044c \u0446\u0435\u043d\u0430: \u0446\u0435\u043b\u044b\u0435 \u0447\u0438\u0441\u043b\u0430 NumPy \u0438\u043c\u0435\u044e\u0442 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u0443\u044e \u0448\u0438\u0440\u0438\u043d\u0443, \u0438 \u043f\u0435\u0440\u0435\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u043c\u043e\u043b\u0447\u0430. \u0415\u0441\u043b\u0438 \u0432 \u0442\u043e\u043c \u0436\u0435 \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u0432\u0437\u044f\u0442\u044c 10 \u043c\u0438\u043b\u043b\u0438\u043e\u043d\u043e\u0432 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432, \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0441\u044f \u0442\u0430\u043a:<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\"><span style=\"color: #C678DD\">import<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">numpy<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">as<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">np<\/span>\n<span style=\"color: #E06C75\">n<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #D19A66\">10_000_000<\/span>\n<span style=\"color: #E06C75\">arr<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">np<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">arange<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">n<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">arr<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">dtype<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E5C07B\">int<\/span><span style=\"color: #ABB2BF\">((<\/span><span style=\"color: #E06C75\">arr<\/span> <span style=\"color: #56B6C2\">*<\/span> <span style=\"color: #E06C75\">arr<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">sum<\/span><span style=\"color: #ABB2BF\">()))<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E5C07B\">sum<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">v<\/span> <span style=\"color: #56B6C2\">*<\/span> <span style=\"color: #E06C75\">v<\/span> <span style=\"color: #C678DD\">for<\/span> <span style=\"color: #E06C75\">v<\/span> <span style=\"color: #56B6C2\">in<\/span> <span style=\"color: #E5C07B\">range<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">n<\/span><span style=\"color: #ABB2BF\">)))<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E5C07B\">int<\/span><span style=\"color: #ABB2BF\">((<\/span><span style=\"color: #E06C75\">arr<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">astype<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">np<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">float64<\/span><span style=\"color: #ABB2BF\">)<\/span> <span style=\"color: #56B6C2\">**<\/span> <span style=\"color: #D19A66\">2<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">sum<\/span><span style=\"color: #ABB2BF\">()))<\/span><\/code><\/pre>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\">int64 1291890006563070912\n333333283333335000000\n333333283333335023616\n<\/code><\/pre>\n<p>\u041f\u0435\u0440\u0432\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430 &#8212; \u043c\u0443\u0441\u043e\u0440 \u0431\u0435\u0437 \u0435\u0434\u0438\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0443\u043f\u0440\u0435\u0436\u0434\u0435\u043d\u0438\u044f: \u0441\u0443\u043c\u043c\u0430 \u0432\u044b\u0448\u043b\u0430 \u0437\u0430 \u043f\u0440\u0435\u0434\u0435\u043b int64 (\u043e\u043a\u043e\u043b\u043e 9,2 * 10^18). \u0412\u0442\u043e\u0440\u0430\u044f &#8212; \u0442\u043e\u0447\u043d\u044b\u0439 \u043e\u0442\u0432\u0435\u0442 Python, \u0443 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0446\u0435\u043b\u044b\u0435 \u0447\u0438\u0441\u043b\u0430 \u043d\u0435 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u044b. \u0422\u0440\u0435\u0442\u044c\u044f &#8212; float64: \u043f\u043e\u0440\u044f\u0434\u043e\u043a \u0432\u0435\u0440\u043d\u044b\u0439, \u043d\u043e \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0446\u0438\u0444\u0440\u044b \u043f\u043e\u0442\u0435\u0440\u044f\u043d\u044b, \u043f\u043e\u0442\u043e\u043c\u0443 \u0447\u0442\u043e \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c float64 \u043e\u043a\u043e\u043b\u043e 15-16 \u0437\u043d\u0430\u0447\u0430\u0449\u0438\u0445 \u0446\u0438\u0444\u0440. \u041f\u0440\u0430\u0432\u0438\u043b\u043e \u043f\u0440\u043e\u0441\u0442\u043e\u0435: \u0435\u0441\u043b\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0433\u0443\u0442 \u0432\u044b\u0439\u0442\u0438 \u0437\u0430 int64, \u0441\u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0432 float64 (\u043f\u043e\u043d\u0438\u043c\u0430\u044f \u043f\u043e\u0442\u0435\u0440\u044e \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438) \u0438\u043b\u0438 \u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0439\u0442\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n<p>\u0415\u0441\u0442\u044c \u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0447\u0435\u0441\u0442\u043d\u044b\u0435 \u043c\u0438\u043d\u0443\u0441\u044b. \u0427\u0438\u0441\u0442\u044b\u0439 Python \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u044b\u0439 \u0432 \u0446\u0438\u043a\u043b\u0430\u0445, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043a\u043e\u0434 ML \u043f\u0438\u0448\u0443\u0442 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u043e. \u041f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u043e\u0442\u043e\u043a\u043e\u0432 \u0432 \u043e\u0431\u044b\u0447\u043d\u043e\u0439 \u0441\u0431\u043e\u0440\u043a\u0435 CPython \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0430 GIL; \u043d\u0430\u0447\u0438\u043d\u0430\u044f \u0441 Python 3.13 \u0435\u0441\u0442\u044c \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u0430\u044f \u0441\u0431\u043e\u0440\u043a\u0430 \u0431\u0435\u0437 GIL, \u043d\u043e \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u044e\u0442 \u0435\u0435 \u043d\u0435 \u0432\u0441\u0435. \u0414\u043b\u044f \u043c\u043e\u0431\u0438\u043b\u044c\u043d\u044b\u0445 \u0438 \u0432\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0435\u043c\u044b\u0445 \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432 \u043c\u043e\u0434\u0435\u043b\u044c \u043e\u0431\u044b\u0447\u043d\u043e \u043e\u0431\u0443\u0447\u0430\u044e\u0442 \u043d\u0430 Python, \u0430 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u044e\u0442 \u0447\u0435\u0440\u0435\u0437 \u044d\u043a\u0441\u043f\u043e\u0440\u0442 \u0432 \u0434\u0440\u0443\u0433\u043e\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, ONNX) \u043d\u0430 C++, Java \u0438\u043b\u0438 Swift.<\/p>\n<h2 id=\"s3\">\u0421\u0442\u0435\u043a \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a: \u0447\u0442\u043e \u0437\u0430 \u0447\u0442\u043e \u043e\u0442\u0432\u0435\u0447\u0430\u0435\u0442<\/h2>\n<p>\u0421\u0442\u0430\u0440\u044b\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u00ab\u0434\u0435\u0441\u044f\u0442\u044c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a \u043f\u043e\u0434\u0440\u044f\u0434\u00bb \u043d\u0435 \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0432\u044b\u0431\u0440\u0430\u0442\u044c. \u0423\u0434\u043e\u0431\u043d\u0435\u0435 \u0438\u0434\u0442\u0438 \u043e\u0442 \u0437\u0430\u0434\u0430\u0447\u0438.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u0417\u0430\u0434\u0430\u0447\u0430<\/th>\n<th>\u0427\u0442\u043e \u0432\u0437\u044f\u0442\u044c<\/th>\n<th>\u041a\u043e\u0433\u0434\u0430 \u043d\u0443\u0436\u043d\u043e \u0434\u0440\u0443\u0433\u043e\u0435<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u041c\u0430\u0441\u0441\u0438\u0432\u044b \u0438 \u043b\u0438\u043d\u0435\u0439\u043d\u0430\u044f \u0430\u043b\u0433\u0435\u0431\u0440\u0430<\/td>\n<td>NumPy<\/td>\n<td>\u0414\u0430\u043d\u043d\u044b\u0435 \u043d\u0435 \u043f\u043e\u043c\u0435\u0449\u0430\u044e\u0442\u0441\u044f \u0432 \u043f\u0430\u043c\u044f\u0442\u044c &#8212; Dask, Polars, Spark<\/td>\n<\/tr>\n<tr>\n<td>\u0422\u0430\u0431\u043b\u0438\u0446\u044b: \u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0430, \u043e\u0447\u0438\u0441\u0442\u043a\u0430, \u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u043a\u0430<\/td>\n<td>pandas<\/td>\n<td>\u041e\u0447\u0435\u043d\u044c \u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u0442\u0430\u0431\u043b\u0438\u0446\u044b \u0438 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c &#8212; Polars<\/td>\n<\/tr>\n<tr>\n<td>\u0413\u0440\u0430\u0444\u0438\u043a\u0438<\/td>\n<td>Matplotlib, Seaborn<\/td>\n<td>\u0418\u043d\u0442\u0435\u0440\u0430\u043a\u0442\u0438\u0432\u043d\u044b\u0435 \u0433\u0440\u0430\u0444\u0438\u043a\u0438 \u0432 \u0431\u0440\u0430\u0443\u0437\u0435\u0440\u0435 &#8212; Plotly, Bokeh<\/td>\n<\/tr>\n<tr>\n<td>\u041a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0435 ML \u043d\u0430 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u0445<\/td>\n<td>scikit-learn<\/td>\n<td>\u041d\u0443\u0436\u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0430\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043d\u0430 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u0445 &#8212; \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043d\u044b\u0439 \u0431\u0443\u0441\u0442\u0438\u043d\u0433: XGBoost, LightGBM, CatBoost<\/td>\n<\/tr>\n<tr>\n<td>\u041d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438, \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f, \u0437\u0432\u0443\u043a<\/td>\n<td>PyTorch<\/td>\n<td>\u0423\u0436\u0435 \u0435\u0441\u0442\u044c \u043f\u0440\u043e\u0435\u043a\u0442 \u043d\u0430 TensorFlow\/Keras \u0438\u043b\u0438 JAX<\/td>\n<\/tr>\n<tr>\n<td>\u0413\u043e\u0442\u043e\u0432\u044b\u0435 \u044f\u0437\u044b\u043a\u043e\u0432\u044b\u0435 \u0438 \u0432\u0438\u0437\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438<\/td>\n<td>Hugging Face Transformers (\u043f\u043e\u0432\u0435\u0440\u0445 PyTorch)<\/td>\n<td>\u041d\u0443\u0436\u0435\u043d \u0442\u043e\u043b\u044c\u043a\u043e \u0432\u044b\u0437\u043e\u0432 \u043f\u043e API \u0431\u0435\u0437 \u0441\u0432\u043e\u0435\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f<\/td>\n<\/tr>\n<tr>\n<td>\u0421\u0440\u0435\u0434\u0430 \u0434\u043b\u044f \u044d\u043a\u0441\u043f\u0435\u0440\u0438\u043c\u0435\u043d\u0442\u043e\u0432<\/td>\n<td>Jupyter (JupyterLab, VS Code)<\/td>\n<td>\u041a\u043e\u0434 \u0434\u043b\u044f \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u0435\u043d\u0430 &#8212; \u043e\u0431\u044b\u0447\u043d\u044b\u0435 \u043c\u043e\u0434\u0443\u043b\u0438 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">.py<\/code> \u0438 \u0442\u0435\u0441\u0442\u044b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u041d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0443\u0442\u043e\u0447\u043d\u0435\u043d\u0438\u0439 \u043a \u0442\u0430\u0431\u043b\u0438\u0446\u0435. SciPy &#8212; \u043d\u0430\u0443\u0447\u043d\u044b\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f \u043f\u043e\u0432\u0435\u0440\u0445 NumPy (\u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f, \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430, \u0440\u0430\u0437\u0440\u0435\u0436\u0435\u043d\u043d\u044b\u0435 \u043c\u0430\u0442\u0440\u0438\u0446\u044b); scikit-learn \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0435\u0433\u043e \u0432\u043d\u0443\u0442\u0440\u0438, \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e \u0435\u0433\u043e \u0441\u0442\u0430\u0432\u044f\u0442 \u0440\u0435\u0436\u0435. Keras 3 \u0443\u043c\u0435\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043f\u043e\u0432\u0435\u0440\u0445 TensorFlow, JAX \u0438 PyTorch. PyTorch \u0441\u043e\u0437\u0434\u0430\u043d \u0432 Facebook AI Research, \u0441 2022 \u0433\u043e\u0434\u0430 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0444\u043e\u043d\u0434\u043e\u043c PyTorch Foundation, \u0438 \u044d\u0442\u043e \u043d\u0435 \u00ab\u043d\u043e\u0432\u044b\u0439 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442 \u0431\u0435\u0437 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438\u00bb: \u043f\u043e \u043d\u0435\u043c\u0443 \u0431\u043e\u043b\u044c\u0448\u0435 \u0432\u0441\u0435\u0433\u043e \u0443\u0447\u0435\u0431\u043d\u044b\u0445 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u043e\u0432 \u0432 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438.<\/p>\n<p>\u0418\u0437 \u0441\u0442\u0430\u0440\u044b\u0445 \u043f\u043e\u0434\u0431\u043e\u0440\u043e\u043a \u0447\u0430\u0441\u0442\u043e \u043a\u043e\u0447\u0443\u044e\u0442 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u0440\u0430\u0442\u044c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442: Pattern \u0434\u0430\u0432\u043d\u043e \u043d\u0435 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f; Basemap \u0445\u043e\u0442\u044c \u0438 \u0432\u044b\u043f\u0443\u0441\u043a\u0430\u0435\u0442 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f, \u043d\u043e \u0434\u043b\u044f \u043d\u043e\u0432\u044b\u0445 \u043a\u0430\u0440\u0442 \u0432 \u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u0435 Matplotlib \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u044e\u0442 Cartopy; \u0430 Scrapy \u0438 NLTK \u043a ML \u043e\u0442\u043d\u043e\u0441\u044f\u0442\u0441\u044f \u043a\u043e\u0441\u0432\u0435\u043d\u043d\u043e &#8212; \u044d\u0442\u043e \u0441\u0431\u043e\u0440 \u0432\u0435\u0431-\u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0442\u0435\u043a\u0441\u0442\u0430.<\/p>\n<h2 id=\"s4\">\u041f\u0435\u0440\u0432\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c: \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u043f\u043e\u043b\u043d\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440<\/h2>\n<p>\u0417\u0430\u0434\u0430\u0447\u0430 &#8212; \u0443\u0447\u0435\u0431\u043d\u0430\u044f \u043a\u043e\u043f\u0438\u044f \u0430\u043d\u0442\u0438\u0444\u0440\u043e\u0434\u0430: \u043f\u043e 10 \u0447\u0438\u0441\u043b\u043e\u0432\u044b\u043c \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u0442\u044c, \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043e\u043d\u0430 \u0438\u043b\u0438 \u043d\u0435\u0442. \u0414\u0430\u043d\u043d\u044b\u0435 \u0441\u0438\u043d\u0442\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 (\u0438\u0445 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u0442 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">make_classification<\/code>), \u0437\u0430\u0442\u043e \u0432 \u043d\u0438\u0445 \u0435\u0441\u0442\u044c \u0433\u043b\u0430\u0432\u043d\u0430\u044f \u0442\u0440\u0443\u0434\u043d\u043e\u0441\u0442\u044c \u043d\u0430\u0441\u0442\u043e\u044f\u0449\u0435\u0433\u043e \u0430\u043d\u0442\u0438\u0444\u0440\u043e\u0434\u0430: \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u043e\u043a\u043e\u043b\u043e 5%. \u041d\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0443 \u0443\u0445\u043e\u0434\u0438\u0442 \u0431\u043e\u043b\u044c\u0448\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u0438, \u0447\u0435\u043c \u043d\u0430 \u043c\u043e\u0434\u0435\u043b\u044c.<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\"><span style=\"color: #C678DD\">import<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">pandas<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">as<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">pd<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.datasets<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">make_classification<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.model_selection<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">train_test_split<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.pipeline<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">make_pipeline<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.preprocessing<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">StandardScaler<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.linear_model<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">LogisticRegression<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.dummy<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">DummyClassifier<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.metrics<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">accuracy_score<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">recall_score<\/span>\n\n<span style=\"color: #7F848E\"># \u0423\u0447\u0435\u0431\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \"\u043a\u0430\u043a \u0432 \u0430\u043d\u0442\u0438\u0444\u0440\u043e\u0434\u0435\": 2000 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439, \u0438\u0437 \u043d\u0438\u0445 \u043e\u043a\u043e\u043b\u043e 5% \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445<\/span>\n<span style=\"color: #E06C75\">X<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">make_classification<\/span><span style=\"color: #ABB2BF\">(<\/span>\n    <span style=\"color: #E06C75\">n_samples<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">2000<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">n_features<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">10<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">n_informative<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">6<\/span><span style=\"color: #ABB2BF\">,<\/span>\n    <span style=\"color: #E06C75\">weights<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #ABB2BF\">[<\/span><span style=\"color: #D19A66\">0.95<\/span><span style=\"color: #ABB2BF\">],<\/span> <span style=\"color: #E06C75\">random_state<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">42<\/span><span style=\"color: #ABB2BF\">,<\/span>\n<span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">X<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">pd<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">DataFrame<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">columns<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #ABB2BF\">[<\/span><span style=\"color: #98C379\">f\"f{<\/span><span style=\"color: #E06C75\">i<\/span><span style=\"color: #98C379\">}\"<\/span> <span style=\"color: #C678DD\">for<\/span> <span style=\"color: #E06C75\">i<\/span> <span style=\"color: #56B6C2\">in<\/span> <span style=\"color: #E5C07B\">range<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">10<\/span><span style=\"color: #ABB2BF\">)])<\/span>\n<span style=\"color: #E06C75\">y<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">pd<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">Series<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">name<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #98C379\">\"fraud\"<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">shape<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">value_counts<\/span><span style=\"color: #ABB2BF\">()<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">to_dict<\/span><span style=\"color: #ABB2BF\">())<\/span>\n\n<span style=\"color: #E06C75\">X_train<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">X_test<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y_train<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y_test<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">train_test_split<\/span><span style=\"color: #ABB2BF\">(<\/span>\n    <span style=\"color: #E06C75\">X<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">test_size<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">0.25<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">stratify<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">random_state<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">42<\/span>\n<span style=\"color: #ABB2BF\">)<\/span>\n\n<span style=\"color: #E06C75\">baseline<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">DummyClassifier<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">strategy<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #98C379\">\"most_frequent\"<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">fit<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X_train<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y_train<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">plain<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">make_pipeline<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">StandardScaler<\/span><span style=\"color: #ABB2BF\">(),<\/span> <span style=\"color: #E06C75\">LogisticRegression<\/span><span style=\"color: #ABB2BF\">())<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">fit<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X_train<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y_train<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">balanced<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">make_pipeline<\/span><span style=\"color: #ABB2BF\">(<\/span>\n    <span style=\"color: #E06C75\">StandardScaler<\/span><span style=\"color: #ABB2BF\">(),<\/span> <span style=\"color: #E06C75\">LogisticRegression<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">class_weight<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #98C379\">\"balanced\"<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">fit<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X_train<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y_train<\/span><span style=\"color: #ABB2BF\">)<\/span>\n\n<span style=\"color: #C678DD\">for<\/span> <span style=\"color: #E06C75\">name<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">clf<\/span> <span style=\"color: #56B6C2\">in<\/span> <span style=\"color: #ABB2BF\">[(<\/span><span style=\"color: #98C379\">\"baseline\"<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">baseline<\/span><span style=\"color: #ABB2BF\">),<\/span> <span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">\"plain\"<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">plain<\/span><span style=\"color: #ABB2BF\">),<\/span> <span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">\"balanced\"<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">balanced<\/span><span style=\"color: #ABB2BF\">)]:<\/span>\n    <span style=\"color: #E06C75\">pred<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">clf<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">predict<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X_test<\/span><span style=\"color: #ABB2BF\">)<\/span>\n    <span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">f\"{<\/span><span style=\"color: #E06C75\">name<\/span><span style=\"color: #98C379\">:8} accuracy={<\/span><span style=\"color: #E06C75\">accuracy_score<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">y_test<\/span><span style=\"color: #ABB2BF\">, <\/span><span style=\"color: #E06C75\">pred<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #98C379\">:.3f} \"<\/span>\n          <span style=\"color: #98C379\">f\"recall(fraud)={<\/span><span style=\"color: #E06C75\">recall_score<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">y_test<\/span><span style=\"color: #ABB2BF\">, <\/span><span style=\"color: #E06C75\">pred<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #98C379\">:.3f}\"<\/span><span style=\"color: #ABB2BF\">)<\/span><\/code><\/pre>\n<p>\u0412 \u043a\u043e\u043d\u0441\u043e\u043b\u0438 \u043f\u043e\u044f\u0432\u044f\u0442\u0441\u044f \u0440\u0430\u0437\u043c\u0435\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u0442\u0440\u0438 \u0441\u0442\u0440\u043e\u043a\u0438 \u043c\u0435\u0442\u0440\u0438\u043a:<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\">(2000, 10) {0: 1892, 1: 108}\nbaseline accuracy=0.946 recall(fraud)=0.000\nplain    accuracy=0.968 recall(fraud)=0.407\nbalanced accuracy=0.806 recall(fraud)=0.852\n<\/code><\/pre>\n<p>\u0427\u0442\u043e \u0437\u0434\u0435\u0441\u044c \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u043f\u043e \u0448\u0430\u0433\u0430\u043c:<\/p>\n<ol>\n<li><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">make_classification<\/code> \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0438 \u043c\u0435\u0442\u043a\u0438, \u0430 pandas \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0438\u0445 \u0432 \u0442\u0430\u0431\u043b\u0438\u0446\u0443 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">DataFrame<\/code> \u0438 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">Series<\/code> &#8212; \u0432 \u0442\u0430\u043a\u043e\u043c \u0432\u0438\u0434\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043e\u0431\u044b\u0447\u043d\u043e \u0438 \u043f\u0440\u0438\u0445\u043e\u0434\u044f\u0442 \u0438\u0437 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">pd.read_csv()<\/code>.<\/li>\n<li><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">train_test_split<\/code> \u043e\u0442\u043a\u043b\u0430\u0434\u044b\u0432\u0430\u0435\u0442 25% \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0435 \u0443\u0432\u0438\u0434\u0438\u0442 \u043f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438. <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">stratify=y<\/code> \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442 \u0434\u043e\u043b\u044e \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0432 \u043e\u0431\u0435\u0438\u0445 \u0447\u0430\u0441\u0442\u044f\u0445, <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">random_state=42<\/code> \u0434\u0435\u043b\u0430\u0435\u0442 \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u0435 \u0432\u043e\u0441\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u043c\u044b\u043c.<\/li>\n<li><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">DummyClassifier<\/code> &#8212; \u0431\u0430\u0437\u043e\u0432\u0430\u044f \u043b\u0438\u043d\u0438\u044f: \u0432\u0441\u0435\u0433\u0434\u0430 \u043e\u0442\u0432\u0435\u0447\u0430\u0435\u0442 \u0441\u0430\u043c\u044b\u043c \u0447\u0430\u0441\u0442\u044b\u043c \u043a\u043b\u0430\u0441\u0441\u043e\u043c. \u0411\u0435\u0437 \u043d\u0435\u0435 \u043d\u0435 \u043f\u043e\u043d\u044f\u0442\u044c, \u0445\u043e\u0440\u043e\u0448 \u043b\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442.<\/li>\n<li><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">make_pipeline<\/code> \u0441\u043a\u043b\u0435\u0438\u0432\u0430\u0435\u0442 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0438 \u043b\u043e\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 \u043e\u0434\u0438\u043d \u043e\u0431\u044a\u0435\u043a\u0442. \u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0438 \u0440\u0430\u0437\u0431\u0440\u043e\u0441 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">StandardScaler<\/code> \u0431\u0435\u0440\u0435\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0447\u0430\u0441\u0442\u0438.<\/li>\n<li><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">class_weight=\"balanced\"<\/code> \u0437\u0430\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u043c\u043e\u0434\u0435\u043b\u044c \u0448\u0442\u0440\u0430\u0444\u043e\u0432\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0443 \u043d\u0430 \u0440\u0435\u0434\u043a\u043e\u043c \u043a\u043b\u0430\u0441\u0441\u0435 \u0441\u0438\u043b\u044c\u043d\u0435\u0435, \u043f\u0440\u043e\u043f\u043e\u0440\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e \u0435\u0433\u043e \u0440\u0435\u0434\u043a\u043e\u0441\u0442\u0438.<\/li>\n<\/ol>\n<p>\u0413\u043b\u0430\u0432\u043d\u044b\u0439 \u0443\u0440\u043e\u043a &#8212; \u0432 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0438 \u0442\u0440\u0435\u0445 \u0441\u0442\u0440\u043e\u043a. \u0411\u0430\u0437\u043e\u0432\u0430\u044f \u043b\u0438\u043d\u0438\u044f \u0434\u0430\u0435\u0442 94,6% \u0432\u0435\u0440\u043d\u044b\u0445 \u043e\u0442\u0432\u0435\u0442\u043e\u0432, \u043d\u0435 \u043d\u0430\u0439\u0434\u044f \u043d\u0438 \u043e\u0434\u043d\u043e\u0439 \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438: accuracy \u043d\u0430 \u043d\u0435\u0441\u0431\u0430\u043b\u0430\u043d\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u043e\u0447\u0442\u0438 \u043d\u0438\u0447\u0435\u0433\u043e \u043d\u0435 \u0433\u043e\u0432\u043e\u0440\u0438\u0442. \u041e\u0431\u044b\u0447\u043d\u0430\u044f \u043b\u043e\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f \u0432\u044b\u0438\u0433\u0440\u044b\u0432\u0430\u0435\u0442 \u0443 \u043d\u0435\u0435 \u0432\u0441\u0435\u0433\u043e 2 \u043f\u0443\u043d\u043a\u0442\u0430 accuracy \u0438 \u043b\u043e\u0432\u0438\u0442 \u043b\u0438\u0448\u044c 41% \u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u0442\u0432\u0430. \u0412\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u043b\u043e\u0432\u0438\u0442 85%, \u043d\u043e accuracy \u043f\u0430\u0434\u0430\u0435\u0442 \u0434\u043e 0,806: \u043f\u043b\u0430\u0442\u043e\u0439 \u0441\u0442\u0430\u043b\u0438 \u043b\u043e\u0436\u043d\u044b\u0435 \u0442\u0440\u0435\u0432\u043e\u0433\u0438.<\/p>\n<p>\u041a\u0430\u043a\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0443\u0447\u0448\u0435, \u0440\u0435\u0448\u0430\u0435\u0442 \u0446\u0435\u043d\u0430 \u043e\u0448\u0438\u0431\u043a\u0438, \u0430 \u043d\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0430 \u00ab\u043f\u043e \u043f\u0440\u0438\u0432\u044b\u0447\u043a\u0435\u00bb: \u0435\u0441\u043b\u0438 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u0430\u044f \u043a\u0440\u0430\u0436\u0430 \u0434\u043e\u0440\u043e\u0436\u0435 \u0437\u0432\u043e\u043d\u043a\u0430 \u043a\u043b\u0438\u0435\u043d\u0442\u0443 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438, \u0432\u044b\u0431\u0438\u0440\u0430\u044e\u0442 recall \u0438 \u0432\u0442\u043e\u0440\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c. \u0426\u0438\u0444\u0440\u044b &#8212; \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043e\u0434\u043d\u043e\u0433\u043e \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u044f \u0441\u0438\u043d\u0442\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445, \u0430 \u043d\u0435 \u0433\u0430\u0440\u0430\u043d\u0442\u0438\u044f \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430; \u0434\u043b\u044f \u0447\u0435\u0441\u0442\u043d\u043e\u0439 \u043e\u0446\u0435\u043d\u043a\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 \u043a\u0440\u043e\u0441\u0441-\u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u044e \u0438 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u0443\u044e \u043e\u0442\u043b\u043e\u0436\u0435\u043d\u043d\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0443.<\/p>\n<h2 id=\"s5\">\u0422\u0438\u043f\u0438\u0447\u043d\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430: \u0443\u0442\u0435\u0447\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445<\/h2>\n<p>\u0421\u0430\u043c\u0430\u044f \u0447\u0430\u0441\u0442\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430 \u043d\u043e\u0432\u0438\u0447\u043a\u0430 &#8212; \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0432\u0441\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438, \u0430 \u043f\u043e\u0442\u043e\u043c \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430 \u0435\u0435 \u0447\u0430\u0441\u0442\u0438. \u0422\u0435\u0441\u0442\u043e\u0432\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0443\u0436\u0435 \u00ab\u043f\u043e\u0434\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u044b\u00bb, \u0438 \u043c\u0435\u0442\u0440\u0438\u043a\u0430 \u0437\u0430\u0432\u044b\u0448\u0435\u043d\u0430. \u041d\u0430\u0433\u043b\u044f\u0434\u043d\u0435\u0435 \u0432\u0441\u0435\u0433\u043e \u044d\u0442\u043e \u043d\u0430 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445, \u0433\u0434\u0435 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043d\u0435\u0447\u0435\u0433\u043e:<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\"><span style=\"color: #C678DD\">import<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">numpy<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">as<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">np<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.feature_selection<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">SelectKBest<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">f_classif<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.linear_model<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">LogisticRegression<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.model_selection<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">cross_val_score<\/span>\n<span style=\"color: #C678DD\">from<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #E06C75\">sklearn.pipeline<\/span><span style=\"color: #ABB2BF\"> <\/span><span style=\"color: #C678DD\">import<\/span> <span style=\"color: #E06C75\">make_pipeline<\/span>\n\n<span style=\"color: #E06C75\">rng<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">np<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">random<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">default_rng<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">0<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">X<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">rng<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">normal<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">size<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">100<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #D19A66\">5000<\/span><span style=\"color: #ABB2BF\">))<\/span>   <span style=\"color: #7F848E\"># 5000 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432<\/span>\n<span style=\"color: #E06C75\">y<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">rng<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">integers<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #D19A66\">0<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #D19A66\">2<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">size<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">100<\/span><span style=\"color: #ABB2BF\">)<\/span>   <span style=\"color: #7F848E\"># \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0435 \u043c\u0435\u0442\u043a\u0438: \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043d\u0435\u0447\u0435\u0433\u043e<\/span>\n\n<span style=\"color: #7F848E\"># \u041d\u0435\u0432\u0435\u0440\u043d\u043e: \u043e\u0442\u0431\u043e\u0440 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e \u0412\u0421\u0415\u041c \u0434\u0430\u043d\u043d\u044b\u043c, \u043f\u043e\u0442\u043e\u043c \u043a\u0440\u043e\u0441\u0441-\u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u044f<\/span>\n<span style=\"color: #E06C75\">X_best<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">SelectKBest<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">f_classif<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">k<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">20<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">fit_transform<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">X<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E06C75\">wrong<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">cross_val_score<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">LogisticRegression<\/span><span style=\"color: #ABB2BF\">(),<\/span> <span style=\"color: #E06C75\">X_best<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">cv<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">5<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">mean<\/span><span style=\"color: #ABB2BF\">()<\/span>\n\n<span style=\"color: #7F848E\"># \u0412\u0435\u0440\u043d\u043e: \u043e\u0442\u0431\u043e\u0440 \u0432\u043d\u0443\u0442\u0440\u0438 \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430, \u0437\u0430\u043d\u043e\u0432\u043e \u043d\u0430 \u043a\u0430\u0436\u0434\u043e\u043c \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u043c \u0444\u043e\u043b\u0434\u0435<\/span>\n<span style=\"color: #E06C75\">pipe<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">make_pipeline<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">SelectKBest<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">f_classif<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">k<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">20<\/span><span style=\"color: #ABB2BF\">),<\/span> <span style=\"color: #E06C75\">LogisticRegression<\/span><span style=\"color: #ABB2BF\">())<\/span>\n<span style=\"color: #E06C75\">right<\/span> <span style=\"color: #56B6C2\">=<\/span> <span style=\"color: #E06C75\">cross_val_score<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #E06C75\">pipe<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">X<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">y<\/span><span style=\"color: #ABB2BF\">,<\/span> <span style=\"color: #E06C75\">cv<\/span><span style=\"color: #56B6C2\">=<\/span><span style=\"color: #D19A66\">5<\/span><span style=\"color: #ABB2BF\">)<\/span><span style=\"color: #56B6C2\">.<\/span><span style=\"color: #E06C75\">mean<\/span><span style=\"color: #ABB2BF\">()<\/span>\n\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">f\"\u0441 \u0443\u0442\u0435\u0447\u043a\u043e\u0439:  {<\/span><span style=\"color: #E06C75\">wrong<\/span><span style=\"color: #98C379\">:.2f}\"<\/span><span style=\"color: #ABB2BF\">)<\/span>\n<span style=\"color: #E5C07B\">print<\/span><span style=\"color: #ABB2BF\">(<\/span><span style=\"color: #98C379\">f\"\u0431\u0435\u0437 \u0443\u0442\u0435\u0447\u043a\u0438: {<\/span><span style=\"color: #E06C75\">right<\/span><span style=\"color: #98C379\">:.2f}\"<\/span><span style=\"color: #ABB2BF\">)<\/span><\/code><\/pre>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\">\u0441 \u0443\u0442\u0435\u0447\u043a\u043e\u0439:  0.85\n\u0431\u0435\u0437 \u0443\u0442\u0435\u0447\u043a\u0438: 0.48\n<\/code><\/pre>\n<p>\u041d\u0435\u0432\u0435\u0440\u043d\u044b\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u043f\u043e\u043a\u0430\u0437\u0430\u043b 85% \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u043d\u0430 \u0448\u0443\u043c\u0435: \u043e\u0442\u0431\u043e\u0440 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0432\u044b\u0431\u0440\u0430\u043b \u0442\u0435 20 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e \u0441\u043e\u0432\u043f\u0430\u043b\u0438 \u0441 \u043c\u0435\u0442\u043a\u0430\u043c\u0438 \u0432\u043e \u0432\u0441\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u0431\u0443\u0434\u0443\u0449\u0438\u0435 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0435 \u0444\u043e\u043b\u0434\u044b. \u0418\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 &#8212; \u043b\u044e\u0431\u043e\u0439 \u0448\u0430\u0433, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0443\u0447\u0438\u0442\u0441\u044f \u043d\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 (\u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435, \u043e\u0442\u0431\u043e\u0440 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432, \u0437\u0430\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u043e\u0432), \u043a\u043b\u0430\u0441\u0442\u044c \u0432\u043d\u0443\u0442\u0440\u044c <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">Pipeline<\/code>. \u0422\u043e\u0433\u0434\u0430 \u043e\u043d \u043e\u0431\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0447\u0430\u0441\u0442\u0438, \u0438 \u0447\u0435\u0441\u0442\u043d\u044b\u0435 0,48 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0442 \u0443\u0433\u0430\u0434\u044b\u0432\u0430\u043d\u0438\u044e.<\/p>\n<h2 id=\"s6\">\u0421 \u0447\u0435\u0433\u043e \u043d\u0430\u0447\u0430\u0442\u044c: \u0434\u043e\u0440\u043e\u0436\u043d\u0430\u044f \u043a\u0430\u0440\u0442\u0430 \u0434\u043e \u043f\u0435\u0440\u0432\u043e\u0433\u043e \u043f\u0440\u043e\u0435\u043a\u0442\u0430<\/h2>\n<p>\u0426\u0435\u043b\u044c &#8212; \u043d\u0435 \u00ab\u0432\u044b\u0443\u0447\u0438\u0442\u044c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438\u00bb, \u0430 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0430\u0440\u0442\u0435\u0444\u0430\u043a\u0442: \u043d\u043e\u0443\u0442\u0431\u0443\u043a \u0438\u043b\u0438 \u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u0439 \u0441 \u043c\u043e\u0434\u0435\u043b\u044c\u044e \u043d\u0430 \u0441\u0432\u043e\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445, \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u043b\u0438\u043d\u0438\u0435\u0439 \u0438 \u0447\u0435\u0441\u0442\u043d\u043e\u0439 \u043c\u0435\u0442\u0440\u0438\u043a\u043e\u0439.<\/p>\n<ol>\n<li><strong>Python \u043d\u0430 \u0443\u0440\u043e\u0432\u043d\u0435 \u0443\u0432\u0435\u0440\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u043a\u0440\u0438\u043f\u0442\u0430<\/strong>: \u0441\u043f\u0438\u0441\u043a\u0438, \u0441\u043b\u043e\u0432\u0430\u0440\u0438, \u0444\u0443\u043d\u043a\u0446\u0438\u0438, \u043a\u043b\u0430\u0441\u0441\u044b, \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f. \u041e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0441\u0442\u0430\u0432\u044f\u0442\u0441\u044f \u0442\u0430\u043a: <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">python -m venv .venv<\/code>, \u0437\u0430\u0442\u0435\u043c <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">python -m pip install numpy pandas scikit-learn matplotlib jupyterlab<\/code>.<\/li>\n<li><strong>NumPy \u0438 pandas<\/strong>: \u0438\u043d\u0434\u0435\u043a\u0441\u0430\u0446\u0438\u044f, \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438, \u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u043a\u0430, \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0438, \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 \u0442\u0430\u0431\u043b\u0438\u0446.<\/li>\n<li><strong>\u041c\u0438\u043d\u0438\u043c\u0443\u043c \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u043a\u0438<\/strong>: \u0441\u0440\u0435\u0434\u043d\u0438\u0435 \u0438 \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044f, \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c, \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u043d\u0430\u044f \u043a\u0430\u043a \u00ab\u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f\u00bb, \u0432\u0435\u043a\u0442\u043e\u0440\u044b \u0438 \u043c\u0430\u0442\u0440\u0438\u0446\u044b. \u0413\u043b\u0443\u0431\u0436\u0435 &#8212; \u043f\u043e \u043c\u0435\u0440\u0435 \u043d\u0430\u0434\u043e\u0431\u043d\u043e\u0441\u0442\u0438.<\/li>\n<li><strong>scikit-learn<\/strong>: \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445, \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d, \u043a\u0440\u043e\u0441\u0441-\u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u044f, \u043c\u0435\u0442\u0440\u0438\u043a\u0438, 2-3 \u043c\u043e\u0434\u0435\u043b\u0438 (\u043b\u0438\u043d\u0435\u0439\u043d\u0430\u044f, \u0434\u0435\u0440\u0435\u0432\u043e, \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043d\u044b\u0439 \u0431\u0443\u0441\u0442\u0438\u043d\u0433).<\/li>\n<li><strong>\u0421\u0432\u043e\u0439 \u043f\u0440\u043e\u0435\u043a\u0442<\/strong>: \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u0439 \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u043f\u043e \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0439 \u0432\u0430\u043c \u0442\u0435\u043c\u0435, \u0431\u0430\u0437\u043e\u0432\u0430\u044f \u043b\u0438\u043d\u0438\u044f, \u043c\u043e\u0434\u0435\u043b\u044c, \u0432\u044b\u0431\u043e\u0440 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u043f\u043e \u0446\u0435\u043d\u0435 \u043e\u0448\u0438\u0431\u043a\u0438, \u043a\u043e\u0440\u043e\u0442\u043a\u0438\u0439 \u0432\u044b\u0432\u043e\u0434.<\/li>\n<li><strong>PyTorch<\/strong> &#8212; \u043a\u043e\u0433\u0434\u0430 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u043e\u0441\u0432\u043e\u0435\u043d\u044b \u0438 \u043d\u0443\u0436\u043d\u044b \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f, \u0442\u0435\u043a\u0441\u0442 \u0438\u043b\u0438 \u0437\u0432\u0443\u043a.<\/li>\n<\/ol>\n<p>\u0427\u0435\u0441\u0442\u043d\u043e\u0433\u043e \u0441\u0440\u043e\u043a\u0430 \u00ab\u0437\u0430 N \u043d\u0435\u0434\u0435\u043b\u044c\u00bb \u043d\u0435\u0442: \u043e\u043d \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u0441\u0442\u0430\u0440\u0442\u043e\u0432\u043e\u0433\u043e \u0443\u0440\u043e\u0432\u043d\u044f \u0438 \u0447\u0430\u0441\u043e\u0432 \u0432 \u043d\u0435\u0434\u0435\u043b\u044e. \u041e\u0440\u0438\u0435\u043d\u0442\u0438\u0440 &#8212; \u043f\u0443\u043d\u043a\u0442\u044b 1-5 \u0437\u0430\u043a\u0440\u044b\u0442\u044b, \u043a\u043e\u0433\u0434\u0430 \u0432\u044b \u0441\u0430\u043c\u0438, \u0431\u0435\u0437 \u043f\u043e\u0434\u0441\u043a\u0430\u0437\u043e\u043a, \u0434\u0435\u043b\u0430\u0435\u0442\u0435 \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d \u043d\u0430 \u043d\u043e\u0432\u043e\u043c \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0435 \u0438 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0435, \u043f\u043e\u0447\u0435\u043c\u0443 \u0432\u044b\u0431\u0440\u0430\u043b\u0438 \u044d\u0442\u0443 \u043c\u0435\u0442\u0440\u0438\u043a\u0443.<\/p>\n<h3>\u0415\u0441\u043b\u0438 \u043d\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u043e\u0441\u044c<\/h3>\n<table>\n<thead>\n<tr>\n<th>\u0421\u0438\u043c\u043f\u0442\u043e\u043c<\/th>\n<th>\u041f\u0440\u0438\u0447\u0438\u043d\u0430<\/th>\n<th>\u0427\u0442\u043e \u0434\u0435\u043b\u0430\u0442\u044c<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">pip install sklearn<\/code> \u043f\u0430\u0434\u0430\u0435\u0442 \u0441 \u043e\u0448\u0438\u0431\u043a\u043e\u0439 \u00abThe &#8216;sklearn&#8217; PyPI package is deprecated\u00bb<\/td>\n<td>\u041f\u0430\u043a\u0435\u0442 \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u0442\u0441\u044f \u0438\u043d\u0430\u0447\u0435<\/td>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">python -m pip install scikit-learn<\/code>; \u0432 \u043a\u043e\u0434\u0435 \u0438\u043c\u043f\u043e\u0440\u0442 \u043e\u0441\u0442\u0430\u0435\u0442\u0441\u044f <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">import sklearn<\/code><\/td>\n<\/tr>\n<tr>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">ModuleNotFoundError<\/code>, \u0445\u043e\u0442\u044f \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0430<\/td>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">pip<\/code> \u043f\u043e\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u0430\u043a\u0435\u0442 \u0432 \u0434\u0440\u0443\u0433\u043e\u0439 \u0438\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0430\u0442\u043e\u0440<\/td>\n<td>\u0421\u0442\u0430\u0432\u0438\u0442\u044c \u0447\u0435\u0440\u0435\u0437 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">python -m pip<\/code> \u0442\u0435\u043c \u0436\u0435 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">python<\/code>, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442 \u043a\u043e\u0434; \u0432 Jupyter \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u044f\u0434\u0440\u043e \u0438\u0437 \u0441\u0432\u043e\u0435\u0433\u043e \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f<\/td>\n<\/tr>\n<tr>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">ConvergenceWarning: lbfgs failed to converge<\/code> \u0443 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">LogisticRegression<\/code><\/td>\n<td>\u041f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0440\u0430\u0437\u043d\u043e\u0433\u043e \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0430, \u043c\u0430\u043b\u043e \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u0439<\/td>\n<td>\u0414\u043e\u0431\u0430\u0432\u0438\u0442\u044c <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">StandardScaler<\/code> \u0432 \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d, \u0443\u0432\u0435\u043b\u0438\u0447\u0438\u0442\u044c <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">max_iter<\/code><\/td>\n<\/tr>\n<tr>\n<td>\u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u043f\u043e\u0434\u043e\u0437\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0432\u044b\u0441\u043e\u043a\u0430\u044f<\/td>\n<td>\u0423\u0442\u0435\u0447\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438\u043b\u0438 \u0434\u0443\u0431\u043b\u0438\u043a\u0430\u0442\u044b \u0441\u0442\u0440\u043e\u043a \u0432 train \u0438 test<\/td>\n<td>\u0412\u0441\u0435 \u0448\u0430\u0433\u0438 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 &#8212; \u0432 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">Pipeline<\/code>, \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0434\u0443\u0431\u043b\u0438\u043a\u0430\u0442\u044b \u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u00ab\u0438\u0437 \u0431\u0443\u0434\u0443\u0449\u0435\u0433\u043e\u00bb<\/td>\n<\/tr>\n<tr>\n<td><code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">torch.cuda.is_available()<\/code> \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">False<\/code><\/td>\n<td>\u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0430 CPU-\u0441\u0431\u043e\u0440\u043a\u0430 PyTorch \u0438\u043b\u0438 \u043d\u0435\u0442 \u0434\u0440\u0430\u0439\u0432\u0435\u0440\u0430<\/td>\n<td>\u0421\u0442\u0430\u0432\u0438\u0442\u044c \u0441\u0431\u043e\u0440\u043a\u0443 \u043f\u043e\u0434 \u0441\u0432\u043e\u044e \u0432\u0435\u0440\u0441\u0438\u044e CUDA \u043f\u043e \u0438\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u0438 \u043d\u0430 \u0441\u0430\u0439\u0442\u0435 PyTorch; \u0434\u043b\u044f \u0443\u0447\u0435\u0431\u044b \u0445\u0432\u0430\u0442\u0430\u0435\u0442 CPU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u041f\u0440\u0435\u0434\u0443\u043f\u0440\u0435\u0436\u0434\u0435\u043d\u0438\u0435 \u043e \u0441\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0442\u0430\u043a (\u0432 \u043a\u043e\u043d\u0446\u0435 \u0432\u044b\u0432\u043e\u0434\u0430 \u0438\u0434\u0443\u0442 \u0441\u0441\u044b\u043b\u043a\u0438 \u043d\u0430 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e):<\/p>\n<pre style=\"background:#282c34;color:#abb2bf;border-radius:8px;padding:16px 20px;margin:20px 0;overflow-x:auto;font-size:14px;line-height:1.55;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\"><code style=\"background:none\">ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\nSTOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n\nIncrease the number of iterations to improve the convergence (max_iter=100).\nYou might also want to scale the data as shown in:\n<\/code><\/pre>\n<h2 id=\"s7\">\u0412\u044b\u0432\u043e\u0434\u044b<\/h2>\n<ul>\n<li>Python \u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 &#8212; \u00ab\u043a\u043b\u0435\u0439\u00bb \u043d\u0430\u0434 \u0431\u044b\u0441\u0442\u0440\u044b\u043c\u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430\u043c\u0438 \u043d\u0430 C\/C++\/CUDA: \u043a\u043e\u0434 \u043f\u0438\u0448\u0435\u0442\u0441\u044f \u043d\u0430 Python, \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u0441\u043a\u043e\u043c\u043f\u0438\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u043a\u043e\u0434.<\/li>\n<li>\u0411\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0442\u0435\u043a: NumPy \u0438 pandas \u0434\u043b\u044f \u0434\u0430\u043d\u043d\u044b\u0445, Matplotlib \u0434\u043b\u044f \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0432, scikit-learn \u0434\u043b\u044f \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e ML, PyTorch \u0434\u043b\u044f \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0439; \u0432\u044b\u0431\u0438\u0440\u0430\u0442\u044c \u043f\u043e \u0437\u0430\u0434\u0430\u0447\u0435, \u0430 \u043d\u0435 \u043f\u043e \u0441\u043f\u0438\u0441\u043a\u0443.<\/li>\n<li>\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043c\u043e\u0434\u0435\u043b\u0438 \u0438\u043c\u0435\u0435\u0442 \u0441\u043c\u044b\u0441\u043b \u0442\u043e\u043b\u044c\u043a\u043e \u0440\u044f\u0434\u043e\u043c \u0441 \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u043b\u0438\u043d\u0438\u0435\u0439 \u0438 \u043c\u0435\u0442\u0440\u0438\u043a\u043e\u0439, \u0432\u044b\u0431\u0440\u0430\u043d\u043d\u043e\u0439 \u043f\u043e \u0446\u0435\u043d\u0435 \u043e\u0448\u0438\u0431\u043a\u0438: \u043d\u0430 \u0440\u0435\u0434\u043a\u043e\u043c \u043a\u043b\u0430\u0441\u0441\u0435 accuracy \u043e\u0431\u043c\u0430\u043d\u044b\u0432\u0430\u0435\u0442.<\/li>\n<li>\u041b\u044e\u0431\u0430\u044f \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0443\u0447\u0438\u0442\u0441\u044f \u043d\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0434\u043e\u043b\u0436\u043d\u0430 \u0436\u0438\u0442\u044c \u0432\u043d\u0443\u0442\u0440\u0438 <code style=\"background:#eef0f3;color:#24292f;border-radius:4px;padding:1px 5px;font-size:0.92em;font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace\">Pipeline<\/code>, \u0438\u043d\u0430\u0447\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0430 \u0437\u0430\u0432\u044b\u0448\u0435\u043d\u0430 \u0443\u0442\u0435\u0447\u043a\u043e\u0439.<\/li>\n<li>\u0421\u043a\u043e\u0440\u043e\u0441\u0442\u044c NumPy \u0438\u043c\u0435\u0435\u0442 \u0446\u0435\u043d\u0443: \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0442\u0438\u043f\u044b \u0438 \u043c\u043e\u043b\u0447\u0430\u043b\u0438\u0432\u043e\u0435 \u043f\u0435\u0440\u0435\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 int64.<\/li>\n<\/ul>\n<h2 id=\"s8\">\u0413\u0434\u0435 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \/ \u0441\u0432\u044f\u0437\u044c \u0441 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u043e\u0439<\/h2>\n<p>\u0421\u0432\u044f\u0437\u043a\u0430 Python + scikit-learn + PyTorch \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0432 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0435 \u0441\u043f\u0440\u043e\u0441\u0430 \u0438 \u043e\u0442\u0442\u043e\u043a\u0430, \u0441\u043a\u043e\u0440\u0438\u043d\u0433\u0435, \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u044f\u0445, \u043f\u043e\u0438\u0441\u043a\u0435 \u0430\u043d\u043e\u043c\u0430\u043b\u0438\u0439, \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u043e\u043c \u0437\u0440\u0435\u043d\u0438\u0438 \u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u0442\u0435\u043a\u0441\u0442\u0430. \u0420\u0430\u0437\u043d\u0438\u0446\u0430 \u043c\u0435\u0436\u0434\u0443 \u0443\u0447\u0435\u0431\u043d\u044b\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c \u0438 \u0440\u0430\u0431\u043e\u0447\u0435\u0439 \u0437\u0430\u0434\u0430\u0447\u0435\u0439 &#8212; \u0432 \u0434\u0430\u043d\u043d\u044b\u0445: \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0438, \u0434\u0438\u0441\u0431\u0430\u043b\u0430\u043d\u0441 \u043a\u043b\u0430\u0441\u0441\u043e\u0432, \u0443\u0442\u0435\u0447\u043a\u0438, \u0432\u044b\u0431\u043e\u0440 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u043f\u043e\u0434 \u0431\u0438\u0437\u043d\u0435\u0441-\u0446\u0435\u043b\u044c.<\/p>\n<div class=\"oj-cta\" style=\"background:#fbf4e4;border-left:4px solid #c9962b;border-radius:8px;padding:16px 20px;margin:24px 0\">\n<p style=\"margin:0 0 8px\"><strong>\u041e\u0441\u0432\u043e\u0439\u0442\u0435 \u0442\u0435\u043c\u0443 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435<\/strong><\/p>\n<p>\u042d\u0442\u0438 \u043d\u0430\u0432\u044b\u043a\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u043e, \u0441 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438 \u043d\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445, \u0440\u0430\u0437\u0431\u0438\u0440\u0430\u044e\u0442 \u043d\u0430 \u043a\u0443\u0440\u0441\u0435 <a href=\"https:\/\/otus.ru\/lessons\/ml-basic\/?int_article=piton-i-mashinnoe-obuchenie-chto-pomozhet-razrabotchiku&amp;int_place=article&amp;int_variant=v1\">Machine Learning. Basic<\/a>. \u0427\u0442\u043e\u0431\u044b \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c \u043d\u0430 \u0444\u043e\u0440\u043c\u0430\u0442 \u0438 \u0442\u0435\u043c\u044b, \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u0438\u0439\u0442\u0438 \u043d\u0430 <a href=\"https:\/\/otus.ru\/events\/?int_article=piton-i-mashinnoe-obuchenie-chto-pomozhet-razrabotchiku&amp;int_place=article&amp;int_variant=v1\">\u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u044b\u0435 \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u0435 \u0443\u0440\u043e\u043a\u0438<\/a>.<\/p>\n<\/div>\n<h2 id=\"s9\">FAQ<\/h2>\n<p><strong>\u041c\u043e\u0436\u043d\u043e \u043b\u0438 \u0437\u0430\u043d\u0438\u043c\u0430\u0442\u044c\u0441\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u044b\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c \u043d\u0435 \u043d\u0430 Python?<\/strong><br \/>\n\u0414\u0430: \u0435\u0441\u0442\u044c R \u0434\u043b\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438, Julia \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0440\u0430\u0441\u0447\u0435\u0442\u043e\u0432, \u0430 \u043c\u043e\u0434\u0435\u043b\u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u044e\u0442 \u043d\u0430 C++, Java, Go \u0438 JavaScript. \u041d\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0438 \u044d\u043a\u0441\u043f\u0435\u0440\u0438\u043c\u0435\u043d\u0442\u044b \u0432 \u0438\u043d\u0434\u0443\u0441\u0442\u0440\u0438\u0438 \u0432 \u043e\u0441\u043d\u043e\u0432\u043d\u043e\u043c \u0438\u0434\u0443\u0442 \u043d\u0430 Python \u0438\u0437-\u0437\u0430 \u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u044b.<\/p>\n<p><strong>\u041d\u0443\u0436\u043d\u0430 \u043b\u0438 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442\u0430, \u0447\u0442\u043e\u0431\u044b \u043d\u0430\u0447\u0430\u0442\u044c?<\/strong><br \/>\n\u041d\u0435\u0442. \u041a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0435 ML \u043d\u0430 scikit-learn \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043d\u0430 \u043e\u0431\u044b\u0447\u043d\u043e\u043c \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0435, \u0430 \u0434\u043b\u044f \u043f\u0435\u0440\u0432\u044b\u0445 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0439 \u0445\u0432\u0430\u0442\u0430\u0435\u0442 CPU \u0438\u043b\u0438 \u043e\u0431\u043b\u0430\u0447\u043d\u044b\u0445 \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u043e\u0432 \u0441 GPU.<\/p>\n<p><strong>\u0427\u0442\u043e \u0443\u0447\u0438\u0442\u044c \u043f\u0435\u0440\u0432\u044b\u043c: pandas \u0438\u043b\u0438 PyTorch?<\/strong><br \/>\npandas \u0438 scikit-learn. PyTorch \u0440\u0435\u0448\u0430\u0435\u0442 \u0437\u0430\u0434\u0430\u0447\u0438 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0430 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u0435 \u0438 \u0432\u044b\u0431\u043e\u0440 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u043d\u0443\u0436\u043d\u044b \u0432 \u043b\u044e\u0431\u043e\u043c \u043f\u0440\u043e\u0435\u043a\u0442\u0435 \u0438 \u043f\u0435\u0440\u0435\u043d\u043e\u0441\u044f\u0442\u0441\u044f \u0432 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0431\u0435\u0437 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0439.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f &#8212; \u044d\u0442\u043e \u044f\u0437\u044b\u043a, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043e\u043f\u0438\u0441\u044b\u0432\u0430\u044e\u0442 \u0432\u0435\u0441\u044c \u043f\u0443\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438: \u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0443 \u0438 \u043e\u0447\u0438\u0441\u0442\u043a\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435, \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0443 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0438 \u0437\u0430\u043f\u0443\u0441\u043a. \u041f\u0440\u0438 \u044d\u0442\u043e\u043c \u0442\u044f\u0436\u0435\u043b\u044b\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0430\u0435\u0442 \u043d\u0435 \u0441\u0430\u043c Python, \u0430 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 C, C++, Fortran \u0438 CUDA: Python \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u0438\u043c\u0438 \u043a\u0430\u043a \u0443\u0434\u043e\u0431\u043d\u044b\u0439 \u00ab\u043f\u0443\u043b\u044c\u0442\u00bb. \u041f\u043e\u044d\u0442\u043e\u043c\u0443 ML \u043d\u0430 Python &#8212; \u044d\u0442\u043e \u0432\u0441\u0435\u0433\u0434\u0430 \u0441\u0432\u044f\u0437\u043a\u0430 \u00ab\u044f\u0437\u044b\u043a + [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":3054,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[167,27],"class_list":["post-3050","post","type-post","status-publish","format-standard","has-post-thumbnail","","category-polza","tag-machine-learning","tag-python"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 Python \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f: \u043f\u043e\u0447\u0435\u043c\u0443 \u0432\u044b\u0431\u0438\u0440\u0430\u044e\u0442 \u0435\u0433\u043e, \u0437\u0430\u0447\u0435\u043c NumPy, pandas, scikit-learn \u0438 PyTorch, \u043f\u0435\u0440\u0432\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0441 \u043a\u043e\u0434\u043e\u043c \u0438 \u0442\u0438\u043f\u0438\u0447\u043d\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430 \u043d\u043e\u0432\u0438\u0447\u043a\u0430.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"A. 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