{"id":13567,"date":"2024-01-02T10:03:08","date_gmt":"2024-01-02T06:33:08","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d9%85%d8%b1%d8%b2%d9%87%d8%a7%db%8c-%d8%aa%d8%b5%d9%85%db%8c%d9%85-%da%af%db%8c%d8%b1%db%8c-%d8%b1%d8%a7-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-python-%d9%88-scikit-lear\/"},"modified":"2024-01-02T10:03:08","modified_gmt":"2024-01-02T06:33:08","slug":"%d9%85%d8%b1%d8%b2%d9%87%d8%a7%db%8c-%d8%aa%d8%b5%d9%85%db%8c%d9%85-%da%af%db%8c%d8%b1%db%8c-%d8%b1%d8%a7-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-python-%d9%88-scikit-lear","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d9%85%d8%b1%d8%b2%d9%87%d8%a7%db%8c-%d8%aa%d8%b5%d9%85%db%8c%d9%85-%da%af%db%8c%d8%b1%db%8c-%d8%b1%d8%a7-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-python-%d9%88-scikit-lear\/","title":{"rendered":"\u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Python \u0648 Scikit-Learn \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u062f"},"content":{"rendered":"<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 3<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<p>\u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u062f\u0631\u062e\u062a \u0646\u0647 \u062a\u0646\u0647\u0627 \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0646\u062a\u0627\u06cc\u062c\u0634\u0627\u0646\u060c \u0648 \u0646\u06cc\u0627\u0632 \u0628\u0647 \u062a\u063a\u06cc\u06cc\u0631\u0627\u062a \u06a9\u0645\u062a\u0631 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u06a9\u0627\u0631 \u0628\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 (\u0628\u0647 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u062f\u0631 \u0648\u0631\u0648\u062f\u06cc \u0648 \u062a\u063a\u06cc\u06cc\u0631\u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0645\u0642\u06cc\u0627\u0633)\u060c \u0628\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062d\u0628\u0648\u0628\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0628\u0644\u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u06a9\u0647 \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u06af\u0631\u0641\u062a\u0646 \u0622\u0646 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f. \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u062f\u0627\u062e\u0644 \u0622\u0646\u0647\u0627 \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0686\u0647 \u062e\u0628\u0631 \u0627\u0633\u062a \u0631\u0648\u06cc \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627<\/p>\n<blockquote>\n<p>\u0645\u0627 \u0641\u0631\u0636 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0634\u0645\u0627 \u0628\u0647 \u062a\u0627\u0632\u06af\u06cc \u06cc\u06a9 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u062f\u0631\u062e\u062a \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 a \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0627\u06cc\u062f <a rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/tree.html\">\u062f\u0631\u062e\u062a \u062a\u0635\u0645\u06cc\u0645<\/a> \u0645\u062f\u0644\u060c \u0648 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0631\u0648\u0634 \u0645\u062f\u06cc\u0631\u06cc\u062a \u062f\u0627\u062f\u0647 \u0647\u0627 \u062a\u0648\u0633\u0637 \u062f\u0631\u062e\u062a \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f. <strong>\u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u0647\u0646\u06af\u0627\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06cc\u06a9 \u0646\u0642\u0637\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062a\u0639\u0644\u0642 \u0628\u0647 \u06cc\u06a9 \u06a9\u0644\u0627\u0633 \u0686\u0647 \u062a\u0635\u0645\u06cc\u0645\u0627\u062a\u06cc \u06af\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a!<\/strong><\/p>\n<\/blockquote>\n<p>\u0627\u06cc\u0646 \u0628\u062f\u0627\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0634\u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0647 \u0622\u0646 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f <strong>\u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc<\/strong> \u0627\u0632 \u062f\u0631\u062e\u062a  \u062e\u0648\u0634\u0628\u062e\u062a\u0627\u0646\u0647\u060c Scikit-Learn \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 \u06cc\u06a9 <code>DecisionBoundaryDisplay<\/code> \u062f\u0631 <code>sklearn.inspection<\/code> \u0645\u062f\u0648\u0644.<\/p>\n<p>\u0627\u0628\u062a\u062f\u0627 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0631\u0627\u0628 \u0627\u0633\u0628\u0627\u0628 \u0628\u0627\u0632\u06cc \u0631\u0627 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0642\u0637\u0627\u0631 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> sklearn.datasets <span class=\"hljs-keyword\">import<\/span> load_wine\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n\nSEED = <span class=\"hljs-number\">42<\/span>\n\ndata = load_wine()\nX = data.data\ny = data.target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                                                    random_state=SEED)\n<\/code><\/pre>\n<p>\u067e\u0633 \u0627\u0632 \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 <em>\u062f\u0648 \u0633\u062a\u0648\u0646 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0631\u0633\u06cc\u0645 \u0645\u0631\u0632 \u062a\u0635\u0645\u06cc\u0645 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u062f<\/em>\u060c \u0645\u062a\u0646\u0627\u0633\u0628 \u0628\u0627 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062f\u0631\u062e\u062a \u0631\u0648\u06cc \u0622\u0646\u0647\u0627 \u0631\u0627\u060c \u0648 \u0637\u0631\u062d \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<span class=\"hljs-keyword\">from<\/span> sklearn.inspection <span class=\"hljs-keyword\">import<\/span> DecisionBoundaryDisplay\n<span class=\"hljs-keyword\">from<\/span> sklearn.tree <span class=\"hljs-keyword\">import<\/span> DecisionTreeClassifier \n\n\nX_train_cols = X_train(:, :<span class=\"hljs-number\">2<\/span>) \n\n\nclassifier = DecisionTreeClassifier(max_depth=<span class=\"hljs-number\">4<\/span>, \n                                    random_state=SEED).fit(X_train_cols, y_train)\n\n\ndisp = DecisionBoundaryDisplay.from_estimator(classifier, \n                                              X_train_cols, \n                                              response_method=<span class=\"hljs-string\">\"predict\"<\/span>,\n                                              xlabel=data.feature_names(<span class=\"hljs-number\">0<\/span>), ylabel=data.feature_names(<span class=\"hljs-number\">1<\/span>),\n                                              alpha=<span class=\"hljs-number\">0.5<\/span>, \n                                              cmap=plt.cm.coolwarm)\n\n\ndisp.ax_.scatter(X_train(:, <span class=\"hljs-number\">0<\/span>), X_train(:, <span class=\"hljs-number\">1<\/span>), \n                 c=y_train, edgecolor=<span class=\"hljs-string\">\"k\"<\/span>,\n                 cmap=plt.cm.coolwarm)\n\nplt.title(<span class=\"hljs-string\">f\"Decision surface for tree trained \u0631\u0648\u06cc <span class=\"hljs-subst\">{data.feature_names(<span class=\"hljs-number\">0<\/span>)}<\/span> and <span class=\"hljs-subst\">{data.feature_names(<span class=\"hljs-number\">1<\/span>)}<\/span>\n\"<\/span>)\nplt.show()\n<\/code><\/pre>\n<p>\u0646\u0645\u0648\u062f\u0627\u0631 \u062d\u0627\u0635\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/plot-tree-decision-boundaries-1.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u0635\u0644\u06cc \u0634\u0631\u0627\u0628 \u062f\u0627\u0631\u0627\u06cc 13 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a\u060c \u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f \u06a9\u0647 \u0645\u0627 \u0641\u0642\u0637 2 \u0633\u062a\u0648\u0646 \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645 &#8211; <code>alcohol<\/code> \u0648 <code>malic_acid<\/code>\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0627\u0634\u0646\u062f <em>\u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0648 \u0628\u0639\u062f \u0631\u0633\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a<\/em> \u0628\u0647 \u062c\u0627\u06cc 13 <em>\u0648 \u062a\u0645\u0627\u0645 &#8211; \u0634\u0645\u0627 \u0641\u0642\u0637 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Python \u0648 Scikit-Learn \u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f!<\/em> \u0627\u0645\u0627\u060c \u0627\u06af\u0631 \u0639\u0644\u0627\u0642\u0647 \u062f\u0627\u0631\u06cc\u062f \u0628\u0647 \u0686\u0646\u062f \u0646\u0645\u0648\u0646\u0647 \u062f\u06cc\u06af\u0631 \u0646\u06af\u0627\u0647\u06cc \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f &#8211; \u0628\u0647 \u062e\u0648\u0627\u0646\u062f\u0646 \u0627\u062f\u0627\u0645\u0647 \u062f\u0647\u06cc\u062f!<\/p>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f<\/strong>: \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u0631\u0648\u0634\u06cc \u0645\u0627\u0646\u0646\u062f PCA \u0627\u0628\u0639\u0627\u062f \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0647 2 \u06a9\u0627\u0647\u0634 \u062f\u0647\u06cc\u062f \u0648 \u0633\u067e\u0633 \u0645\u0631\u0632 \u062a\u0635\u0645\u06cc\u0645 \u0645\u062f\u0644 \u0631\u0627 \u0631\u0633\u0645 \u06a9\u0646\u06cc\u062f.  \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u0646\u062c\u0631 \u0628\u0647 \u0627\u06cc\u062c\u0627\u062f \u0647\u0645\u0627\u0646 \u06a9\u062f \u0642\u0628\u0644\u06cc \u0645\u06cc \u0634\u0648\u062f \u0648 \u0641\u0642\u0637 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0645\u06cc \u0634\u0648\u062f <code>X_train_cols<\/code> \u0628\u0631\u0627\u06cc \u0627\u062c\u0632\u0627\u06cc \u0627\u0635\u0644\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647<\/p>\n<\/p><\/div><\/div><\/div>\n<h2 id=\"plottingdecisionboundariesadditionalexample\">\u062a\u0631\u0633\u06cc\u0645 \u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 &#8211; \u0645\u062b\u0627\u0644 \u0627\u0636\u0627\u0641\u06cc<\/h2>\n<p>\u0627\u06af\u0631 \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u062a\u0631\u06a9\u06cc\u0628\u200c\u0647\u0627\u06cc \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631 \u062c\u0627\u0644\u0628 \u0627\u0633\u062a\u060c \u062f\u0631 \u0632\u06cc\u0631 \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u062a\u0631\u0633\u06cc\u0645 \u062a\u0631\u06a9\u06cc\u0628\u200c\u0647\u0627 \u0628\u0631\u0627\u06cc 5 \u0633\u062a\u0648\u0646 \u0627\u0648\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0631\u0627\u0628 \u0622\u0648\u0631\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.<\/p>\n<p>\u06af\u0627\u0645 \u0627\u0648\u0644\u06cc\u0647 \u062a\u0648\u0644\u06cc\u062f \u062a\u0645\u0627\u0645 \u062a\u0631\u06a9\u06cc\u0628\u0627\u062a \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u0627\u0632 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> itertools <span class=\"hljs-keyword\">import<\/span> combinations\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n\n\ncomb = combinations(np.arange(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">6<\/span>), <span class=\"hljs-number\">2<\/span>)\n\n\nunique_combinations = <span class=\"hljs-built_in\">set<\/span>(comb) \n\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 <code>unique_combinations<\/code> \u0633\u062a\u0648\u0646 \u0647\u0627\u06cc\u06cc \u062e\u0648\u0627\u0647\u0646\u062f \u0628\u0648\u062f \u06a9\u0647 \u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u0628\u0631\u0627\u06cc \u0622\u0646\u0647\u0627 \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u0634\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">\nn_classes = <span class=\"hljs-number\">3<\/span>\ncolor_palette = plt.cm.coolwarm\nplot_colors = <span class=\"hljs-string\">\"bwr\"<\/span> \nplot_step = <span class=\"hljs-number\">0.02<\/span>\n\nplt.figure(figsize=(<span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">12<\/span>))\n\n<span class=\"hljs-keyword\">for<\/span> pair_idx, pair <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">enumerate<\/span>(<span class=\"hljs-built_in\">sorted<\/span>(unique_combinations)):\n    \n    X_train_cols = X_train(:, pair)\n\n    \n    classifier = DecisionTreeClassifier(max_depth=<span class=\"hljs-number\">4<\/span>, \n                                        random_state=SEED).fit(X_train_cols, y_train)\n\n    \n    ax = plt.subplot(<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">5<\/span>, pair_idx + <span class=\"hljs-number\">1<\/span>)\n    \n    DecisionBoundaryDisplay.from_estimator(classifier,\n                                           X_train_cols,\n                                           cmap=color_palette,\n                                           response_method=<span class=\"hljs-string\">\"predict\"<\/span>,\n                                           ax=ax,\n                                           xlabel=data.feature_names(pair(<span class=\"hljs-number\">0<\/span>)),\n                                           ylabel=data.feature_names(pair(<span class=\"hljs-number\">1<\/span>)),\n                                           alpha = <span class=\"hljs-number\">0.5<\/span>)\n\n    \n    <span class=\"hljs-keyword\">for<\/span> i, color <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">zip<\/span>(<span class=\"hljs-built_in\">range<\/span>(n_classes), plot_colors):\n        idx = np.where(y_train == i)\n        plt.scatter(X_train_cols(idx, <span class=\"hljs-number\">0<\/span>),\n                    X_train_cols(idx, <span class=\"hljs-number\">1<\/span>),\n                    c=color,\n                    label=data.target_names(i),\n                    cmap=color_palette,\n                    edgecolor=<span class=\"hljs-string\">\"black\"<\/span>,\n                    s=<span class=\"hljs-number\">15<\/span>)\n\nplt.suptitle(<span class=\"hljs-string\">\"Decision surface of decision trees trained \u0631\u0648\u06cc pairs of features\"<\/span>, fontsize=<span class=\"hljs-number\">14<\/span>)\nplt.legend(loc=<span class=\"hljs-string\">\"lower right\"<\/span>);\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u06a9\u062f \u0646\u0645\u0648\u062f\u0627\u0631 \u0632\u06cc\u0631 \u0631\u0627 \u0646\u0645\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/plot-tree-decision-boundaries-2.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0647\u0631 \u062f\u0648 \u0637\u0631\u062d \u062a\u0635\u0645\u06cc\u0645\u200c\u06af\u06cc\u0631\u06cc \u0633\u0641\u0627\u0631\u0634\u06cc\u200c\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u062f\u0627\u0631\u0646\u062f\u060c \u0645\u0627\u0646\u0646\u062f \u067e\u0627\u0644\u062a\u200c\u0647\u0627\u06cc \u0631\u0646\u06af\u060c \u062a\u06cc\u0631\u06af\u06cc\u060c \u0641\u0636\u0627\u06cc \u0628\u06cc\u0646 \u0637\u0631\u062d \u0648 \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0648 \u063a\u06cc\u0631\u0647. \u0633\u0639\u06cc \u06a9\u0646\u06cc\u062f \u0628\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0628\u0627\u0632\u06cc \u06a9\u0646\u06cc\u062f \u062a\u0627 \u0647\u0631 \u0642\u0633\u0645\u062a \u0627\u0632 \u06a9\u062f \u0631\u0627 \u0628\u0647\u062a\u0631 \u062f\u0631\u06a9 \u06a9\u0646\u06cc\u062f!<\/p>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> \u0627\u06cc\u0646 <code>DecisionBoundaryDisplay<\/code> \u0628\u0647 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u062f\u0631\u062e\u062a \u0645\u062d\u062f\u0648\u062f \u0646\u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0622\u0646 \u0628\u0627 \u0628\u0631\u0622\u0648\u0631\u062f\u06af\u0631\u0647\u0627\u06cc \u062f\u06cc\u06af\u0631 \u062f\u0631 Scikit-learn \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f\u060c <a rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.inspection.DecisionBoundaryDisplay.html?highlight=decisionboundarydisplay#sklearn.inspection.DecisionBoundaryDisplay\">\u0645\u0633\u062a\u0646\u062f\u0627\u062a<\/a> \u0628\u0631\u0627\u06cc \u062f\u0627\u0646\u0633\u062a\u0646 \u0628\u06cc\u0634\u062a\u0631 \u062f\u0631 \u0645\u0648\u0631\u062f \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc \u062f\u06cc\u06af\u0631<\/p>\n<\/p><\/div><\/div><\/div><\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-02 10:03:03<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;13567&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u0645\u0631\u0632\u0647\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Python \u0648 Scikit-Learn \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u062f&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 3<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u062f\u0631\u062e\u062a \u0646\u0647 \u062a\u0646\u0647\u0627 \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0646\u062a\u0627\u06cc\u062c\u0634\u0627\u0646\u060c \u0648 \u0646\u06cc\u0627\u0632 \u0628\u0647 \u062a\u063a\u06cc\u06cc\u0631\u0627\u062a \u06a9\u0645\u062a\u0631 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u06a9\u0627\u0631 \u0628\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 (\u0628\u0647 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u062f\u0631 \u0648\u0631\u0648\u062f\u06cc \u0648 \u062a\u063a\u06cc\u06cc\u0631\u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0645\u0642\u06cc\u0627\u0633)\u060c \u0628\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062d\u0628\u0648\u0628\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0628\u0644\u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u06a9\u0647 \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u06af\u0631\u0641\u062a\u0646 \u0622\u0646 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f. \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u062f\u0627\u062e\u0644 \u0622\u0646\u0647\u0627 \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0686\u0647 \u062e\u0628\u0631 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":13568,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-13567","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/13567","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=13567"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/13567\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/13568"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=13567"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=13567"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=13567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}