{"id":1164,"date":"2023-07-27T10:43:39","date_gmt":"2023-07-27T10:43:39","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-linear-discriminant-analysis\/"},"modified":"2023-07-27T10:43:39","modified_gmt":"2023-07-27T10:43:39","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-linear-discriminant-analysis","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-linear-discriminant-analysis\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a linear discriminant analysis (\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/my\/linear-\u1001\u103d\u1032\u1001\u103c\u102c\u1038\u1019\u103e\u102f-\u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1010\u103a\u1016\u103c\u102c\u1001\u103c\u1004\u103a\u1038\u104b\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u1010\u1005\u103a\u1015\u103c\u1031\u1038\u100a\u102e \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u1019\u103e\u102f \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1010\u103a\u1016\u103c\u102c\u1001\u103c\u1004\u103a\u1038<\/a> \u101e\u100a\u103a \u101e\u1004\u1037\u103a\u1010\u103d\u1004\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038 \u1021\u1005\u102f\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u104d <a href=\"https:\/\/statorials.org\/my\/\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c-\u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1001\u103b\u1000\u103a-\u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1000\u102d\u102f<\/a> \u1021\u1010\u1014\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1011\u102d\u102f\u1037\u1011\u1000\u103a\u1015\u102d\u102f\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1021\u1005\u102c\u1038\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u101c\u102d\u102f\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a \u101e\u1004\u103a\u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a linear discriminant analysis \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c\u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1026\u1038\u1005\u103d\u102c\u104a \u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1012\u1005\u103a\u1002\u103b\u1005\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u1031\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedStratifiedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> cross_val_score\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">discriminant_analysis<\/span> <span style=\"color: #008000;\">import<\/span> LinearDiscriminantAnalysis \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/b><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 2: \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a sklearn \u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103e <strong>iris<\/strong> dataset \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1024\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1010\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u1015\u103c\u102e\u1038 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101b\u1014\u103a \u1015\u1014\u103a\u1012\u102b DataFrame \u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>iris<\/em> dataset<\/span>\niris = datasets. <span style=\"color: #3366ff;\">load_iris<\/span> ()\n\n<span style=\"color: #008080;\">#convert dataset to pandas DataFrame\n<\/span>df = pd.DataFrame(data = np.c_[iris[' <span style=\"color: #008000;\">data<\/span> '], iris[' <span style=\"color: #008000;\">target<\/span> ']],\n                 columns = iris[' <span style=\"color: #008000;\">feature_names<\/span> '] + [' <span style=\"color: #008000;\">target<\/span> '])\ndf[' <span style=\"color: #008000;\">species<\/span> '] = pd. <span style=\"color: #3366ff;\">Categorical<\/span> . <span style=\"color: #3366ff;\">from_codes<\/span> (iris.target, iris.target_names)\ndf.columns = [' <span style=\"color: #008000;\">s_length<\/span> ', ' <span style=\"color: #008000;\">s_width<\/span> ', ' <span style=\"color: #008000;\">p_length<\/span> ', ' <span style=\"color: #008000;\">p_width<\/span> ', ' <span style=\"color: #008000;\">target<\/span> ', ' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#view first six rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n   s_length s_width p_length p_width target species\n0 5.1 3.5 1.4 0.2 0.0 setosa\n1 4.9 3.0 1.4 0.2 0.0 setosa\n2 4.7 3.2 1.3 0.2 0.0 setosa\n3 4.6 3.1 1.5 0.2 0.0 setosa\n4 5.0 3.6 1.4 0.2 0.0 setosa\n\n<span style=\"color: #3366ff;\"><span style=\"color: #008080;\">#find how many total observations are in dataset<\/span>\n<span style=\"color: #000000;\">len( <span style=\"color: #3366ff;\">df.index<\/span> )\n\n150<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u1019\u103e\u102f\u1015\u1031\u102b\u1004\u103a\u1038 \u1041\u1045\u1040 \u1015\u102b\u101d\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1015\u1031\u1038\u1011\u102c\u1038\u101e\u1031\u102c\u1015\u1014\u103a\u1038\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1001\u103d\u1032\u1001\u103c\u102c\u1038\u101b\u1014\u103a linear discriminant analysis model \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1010\u100a\u103a\u1006\u1031\u102c\u1000\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u103d\u1004\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1014\u103e\u102f\u1010\u103a\u1001\u1019\u103a\u1038\u101e\u102c\u1038\u1021\u101b\u103e\u100a\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">Sepal \u1021\u1000\u103b\u101a\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1015\u103d\u1004\u1037\u103a\u1001\u103b\u1015\u103a\u1021\u101b\u103e\u100a\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1015\u103d\u1004\u1037\u103a\u1001\u103b\u1015\u103a\u1021\u1000\u103b\u101a\u103a<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1016\u103c\u1005\u103a\u1014\u102d\u102f\u1004\u103a\u1001\u103b\u1031\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u101e\u102f\u1036\u1038\u1019\u103b\u102d\u102f\u1038\u1021\u102c\u1038 \u1015\u1036\u1037\u1015\u102d\u102f\u1038\u1015\u1031\u1038\u101e\u100a\u1037\u103a <em>Species<\/em> response variable \u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">setosa<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1005\u103d\u101a\u103a\u1005\u102f\u1036\u101b\u1031\u102c\u1004\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1017\u102c\u1002\u103b\u102e\u1038\u1014\u102e\u1038\u101a\u102c\u1038<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 3- LDA \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a sklearn \u104f <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">LinearDiscriminantAnalsys<\/a> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a LDA \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u1036\u1000\u102d\u102f\u1000\u103a\u101c\u102f\u1015\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = df[[' <span style=\"color: #008000;\">s_length<\/span> ',' <span style=\"color: #008000;\">s_width<\/span> ',' <span style=\"color: #008000;\">p_length<\/span> ',' <span style=\"color: #008000;\">p_width<\/span> ']]\ny = df[' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#Fit the LDA model\n<\/span>model = LinearDiscriminantAnalysis()\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 4- \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1010\u1015\u103a\u1006\u1004\u103a\u1015\u103c\u102e\u1038\u101e\u100a\u103a\u1014\u103e\u1004\u1037\u103a\u104a \u1011\u1015\u103a\u1001\u102b\u1010\u101c\u1032\u101c\u1032 stratified k-fold cross-validation \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u104f\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f \u1021\u1000\u1032\u1016\u103c\u1010\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a 10 \u1001\u1031\u102b\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1011\u1015\u103a\u1001\u102b\u1010\u101c\u1032\u101c\u1032 3 \u1001\u102f\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#Define method to evaluate model\n<span style=\"color: #000000;\">cv = RepeatedStratifiedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#evaluate model\n<span style=\"color: #000000;\">scores = cross_val_score(model, X, y, scoring=' <span style=\"color: #008000;\">accuracy<\/span> ', cv=cv, n_jobs=-1)\nprint( <span style=\"color: #3366ff;\">np.mean<\/span> (scores))<\/span>  \n\n<span style=\"color: #000000;\">0.9777777777777779<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1010\u102d\u1000\u103b\u1019\u103e\u102f <strong>97.78%<\/strong> \u101b\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1015\u1014\u103a\u1038\u1021\u101e\u1005\u103a\u1010\u1005\u103a\u1001\u102f\u104f \u1021\u1010\u1014\u103a\u1038\u1021\u1005\u102c\u1038 \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<\/span>new = [5, 3, 1, .4]\n\n<span style=\"color: #008080;\">#predict which class the new observation belongs to\n<\/span>model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray(['setosa'], dtype='&lt;U10')\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1021\u101e\u1005\u103a\u101e\u100a\u103a <em>setosa<\/em> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c\u1019\u103b\u102d\u102f\u1038\u1005\u102d\u1010\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u101e\u1000\u103a\u1006\u102d\u102f\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u101b\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 5- \u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u104f \u1010\u1005\u103a\u1015\u103c\u1031\u1038\u100a\u102e \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u1006\u1000\u103a\u1006\u1036\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101e\u102c\u1005\u1031\u101b\u1014\u103a LDA \u1000\u103c\u1036\u1005\u100a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u102d\u102f\u1004\u103a\u1015\u103c\u102e\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1019\u103b\u102d\u102f\u1038\u1005\u102d\u1010\u103a\u101e\u102f\u1036\u1038\u1019\u103b\u102d\u102f\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u1019\u103b\u103e\u1000\u1031\u102c\u1004\u103a\u1038\u1005\u103d\u102c \u1015\u102d\u102f\u1004\u103a\u1038\u1001\u103c\u102c\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data to plot\n<\/span>X = iris.data\ny = iris.target\nmodel = LinearDiscriminantAnalysis()\ndata_plot = model. <span style=\"color: #3366ff;\">fit<\/span> (x,y). <span style=\"color: #3366ff;\">transform<\/span> (X)\ntarget_names = iris. <span style=\"color: #3366ff;\">target_names<\/span>\n\n<span style=\"color: #008080;\">#create LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">figure<\/span> ()\ncolors = [' <span style=\"color: #008000;\">red<\/span> ', ' <span style=\"color: #008000;\">green<\/span> ', ' <span style=\"color: #008000;\">blue<\/span> ']\nlw = 2\n<span style=\"color: #008000;\">for<\/span> color, i, target_name <span style=\"color: #008000;\">in<\/span> zip(colors, [0, 1, 2], target_names):\n    plt. <span style=\"color: #3366ff;\">scatter<\/span> (data_plot[y == i, 0], data_plot[y == i, 1], alpha=.8, color=color,\n                label=target_name)\n\n<span style=\"color: #008080;\">#add legend to plot\n<\/span>plt. <span style=\"color: #3366ff;\">legend<\/span> (loc=' <span style=\"color: #008000;\">best<\/span> ', shadow= <span style=\"color: #008000;\">False<\/span> , scatterpoints=1)\n\n<span style=\"color: #008080;\">#display LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11651 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ldapython1.png\" alt=\"Python \u1010\u103d\u1004\u103a linear discriminant analysis\" width=\"416\" height=\"281\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1011\u102c\u1038\u101e\u1031\u102c Python \u1000\u102f\u1012\u103a\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u1010\u1005\u103a\u1015\u103c\u1031\u1038\u100a\u102e \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u1019\u103e\u102f \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1010\u103a\u1016\u103c\u102c\u1001\u103c\u1004\u103a\u1038 \u101e\u100a\u103a \u101e\u1004\u1037\u103a\u1010\u103d\u1004\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038 \u1021\u1005\u102f\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u104d \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1000\u102d\u102f \u1021\u1010\u1014\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1011\u102d\u102f\u1037\u1011\u1000\u103a\u1015\u102d\u102f\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1021\u1005\u102c\u1038\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u101c\u102d\u102f\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a \u101e\u1004\u103a\u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a linear discriminant analysis \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b \u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c\u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b \u1026\u1038\u1005\u103d\u102c\u104a \u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1012\u1005\u103a\u1002\u103b\u1005\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u1031\u1038\u1015\u102b\u1019\u100a\u103a\u104b from sklearn. model_selection import train_test_split from sklearn. model_selection import RepeatedStratifiedKFold from sklearn. model_selection import cross_val_score from sklearn. discriminant_analysis import LinearDiscriminantAnalysis from sklearn [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u1010\u103d\u1004\u103a Linear Discriminant Analysis 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