{"id":1210,"date":"2023-07-27T06:54:42","date_gmt":"2023-07-27T06:54:42","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%85%e1%80%ba%e1%80%85%e1%80%ad%e1%80%90%e1%80%ba%e1%80%90%e1%80%85%e1%80%ba%e1%80%95%e1%80%ad%e1%80%af%e1%80%84%e1%80%ba%e1%80%b8-%e1%80%a1\/"},"modified":"2023-07-27T06:54:42","modified_gmt":"2023-07-27T06:54:42","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%85%e1%80%ba%e1%80%85%e1%80%ad%e1%80%90%e1%80%ba%e1%80%90%e1%80%85%e1%80%ba%e1%80%95%e1%80%ad%e1%80%af%e1%80%84%e1%80%ba%e1%80%b8-%e1%80%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%85%e1%80%ba%e1%80%85%e1%80%ad%e1%80%90%e1%80%ba%e1%80%90%e1%80%85%e1%80%ba%e1%80%95%e1%80%ad%e1%80%af%e1%80%84%e1%80%ba%e1%80%b8-%e1%80%a1\/","title":{"rendered":"Python \u101b\u103e\u102d \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038 \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038 (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">machine learning \u1010\u103d\u1004\u103a \u101e\u1004\u103a\u1000\u103c\u102f\u1036\u1010\u103d\u1031\u1037\u101b\u1019\u100a\u1037\u103a \u1021\u1016\u103c\u1005\u103a\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1015\u103c\u103f\u1014\u102c\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c <a href=\"https:\/\/statorials.org\/my\/multicollinearity-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener noreferrer\">multicollinearity<\/a> \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \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 \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 \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u101c\u103d\u1014\u103a\u1006\u1000\u103a\u1005\u1015\u103a\u1014\u1031\u101e\u1031\u102c\u1021\u1001\u102b \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1016\u103c\u1005\u103a\u1015\u1031\u102b\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u101e\u102d\u102f\u1037\u1016\u103c\u1005\u103a\u101c\u102c\u101e\u1031\u102c\u1021\u1001\u102b \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1000\u1031\u102c\u1004\u103a\u1038\u1005\u103d\u102c\u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e <a href=\"https:\/\/statorials.org\/my\/\u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f-\u1021\u101c\u103d\u1014\u103a\u1021\u1000\u103b\u103d\u1036\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u101e\u1031\u102c<\/a> \u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1019\u1019\u103c\u1004\u103a\u1016\u1030\u1038\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1021\u101e\u1005\u103a\u1010\u103d\u1004\u103a \u100a\u1036\u1037\u1016\u103b\u1004\u103a\u1038\u1005\u103d\u102c\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1019\u100a\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1015\u103c\u103f\u1014\u102c\u1000\u102d\u102f \u1016\u103c\u1031\u101b\u103e\u1004\u103a\u1038\u101b\u1014\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c <a href=\"https:\/\/statorials.org\/my\/\u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038-\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038-\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\/\" target=\"_blank\" rel=\"noopener noreferrer\">partial least squares<\/a> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1014\u103e\u1004\u1037\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1005\u1036\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u102b\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1004\u1037\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 \u1014\u103e\u1005\u103a\u1001\u102f\u101c\u102f\u1036\u1038\u1010\u103d\u1004\u103a \u101e\u102d\u101e\u102c\u1011\u1004\u103a\u101b\u103e\u102c\u1038\u101e\u1031\u102c\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1019\u103e\u102f\u1015\u1019\u102c\u100f\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u1037\u103a<\/span> <em style=\"color: #000000;\">p<\/em> \u1019\u1030\u101c\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 <span style=\"color: #000000;\">\u1019\u103b\u102c\u1038\u104f <em>M<\/em> linear \u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038 (&#8220; PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1019\u103b\u102c\u1038&#8221; ) \u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1015\u102b<\/span> \u104b<\/li>\n<li> <span style=\"color: #000000;\">\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1014\u103e\u1004\u1037\u103a\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u103d\u1004\u103a \u1011\u102c\u1038\u101b\u103e\u102d\u101b\u1014\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038 PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a <a href=\"https:\/\/statorials.org\/my\/k-cross-validation-\u1000\u102d\u102f-\u1001\u1031\u102b\u1000\u103a\u1015\u102b\u104b\/\" target=\"_blank\" rel=\"noopener noreferrer\">k-fold \u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a validation \u1000\u102d\u102f<\/a> \u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038 \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\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 \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103a\u103b\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c\u104a Python \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038 \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1015\u1000\u103a\u1000\u1031\u1037\u1002\u103b\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\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;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> scale \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> model_selection\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold\n<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 style=\"color: #000000;\">sklearn. <span style=\"color: #3366ff;\">cross_decomposition<\/span> <span style=\"color: #008000;\">import<\/span> PLSRegression<\/span>\n<span style=\"color: #008000;\">from<\/span> <span style=\"color: #000000;\">sklearn<\/span> . <span style=\"color: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> <span style=\"color: #000000;\">mean_squared_error\n<\/span><\/span><\/strong><\/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 \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u102c\u1038 \u1043\u1043 \u1005\u102e\u1038\u101b\u103e\u102d \u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c <strong>mtcars<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f variable \u1021\u1016\u103c\u1005\u103a <strong>hp \u1000\u102d\u102f<\/strong> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u103c\u102e\u1038 \u1021\u1031\u102c\u1000\u103a\u1015\u102b variable \u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1005\u102d\u102f\u1004\u103a\u1038\u1005\u102d\u102f\u1004\u103a\u1038\u1001\u1019\u103a\u1038\u101c\u103e\u102d\u102f\u1004\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1015\u103c\u101e\u1001\u103c\u1004\u103a\u1038\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1015\u103c\u1031\u102c\u101b\u1019\u103e\u102c\u1015\u102b\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1000\u102d\u102f\u101a\u103a\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\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\u104d \u1015\u103c\u101e\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data\n<\/span>data[0:6]\n\n\n        mpg disp drat wt qsec hp\n0 21.0 160.0 3.90 2.620 16.46 110\n1 21.0 160.0 3.90 2.875 17.02 110\n2 22.8 108.0 3.85 2.320 18.61 93\n3 21.4 258.0 3.08 3.215 19.44 110\n4 18.7 360.0 3.15 3.440 17.02 175\n5 18.1 225.0 2.76 3.460 20.22 105<\/strong><\/span><\/pre>\n<h3> <strong>\u1021\u1006\u1004\u1037\u103a 3- \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038 \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038 \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u103d\u1000\u103a\u1010\u102d\u1015\u102b\u104b<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1024\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a PLS \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037 \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b \u1016\u103c\u1005\u103a\u1005\u1031\u101b\u1014\u103a \u1016\u1031\u102c\u103a\u1015\u103c\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>cv = RepeatedKFold() \u101e\u100a\u103a<\/strong> \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f\u1021\u1000\u1032\u1016\u103c\u1010\u103a\u101b\u1014\u103a <a href=\"https:\/\/statorials.org\/my\/k-cross-validation-\u1000\u102d\u102f-\u1001\u1031\u102b\u1000\u103a\u1015\u102b\u104b\/\" target=\"_blank\" rel=\"noopener noreferrer\">k-fold cross-validation \u1000\u102d\u102f<\/a> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a Python \u1021\u102c\u1038 \u1015\u103c\u1031\u102c\u101e\u100a\u103a\u1000\u102d\u102f<\/span> <span style=\"color: #000000;\">\u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b<\/span> \u104b <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a k = 10 \u1001\u1031\u102b\u1000\u103a\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1000\u102c 3 \u1000\u103c\u102d\u1019\u103a \u1011\u1015\u103a\u1001\u102b\u1011\u1015\u103a\u1001\u102b \u101c\u102f\u1015\u103a\u101e\u100a\u103a\u104b<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\"]]\ny = data[[\"hp\"]]\n\n<span style=\"color: #008080;\">#define cross-validation method\n<span style=\"color: #000000;\">cv = RepeatedKFold(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\nmse = []\nn = <span style=\"color: #3366ff;\">len<\/span> (X)<\/span>\n\n# Calculate MSE with only the intercept\n<span style=\"color: #000000;\">score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (PLSRegression(n_components=1),<\/span>\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">ones<\/span> ((n,1)), y, cv=cv, scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()    \nmse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n# Calculate MSE using cross-validation, adding one component at a time\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">for<\/span> i <span style=\"color: #008000;\">in<\/span> np. <span style=\"color: #3366ff;\">arange<\/span> (1, 6):\n    pls = PLSRegression(n_components=i)\n    score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (pls, scale(X), y, cv=cv,\n               scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()\n    mse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n#plot test MSE vs. number of components\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (mse)\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">Number of PLS Components<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">MSE<\/span> ')\nplt. <span style=\"color: #3366ff;\">title<\/span> (' <span style=\"color: #008000;\">hp<\/span> ')<\/span>\n<\/span><\/strong><\/span><\/pre>\n<h3><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11985 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/svppython1.png\" alt=\"Python Cross-Validation Plot \u101b\u103e\u102d \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038 \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\" width=\"405\" height=\"284\" srcset=\"\" sizes=\"\"><\/h3>\n<p> <span style=\"color: #000000;\">\u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u101e\u100a\u103a x-\u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1010\u1005\u103a\u101c\u103b\u103e\u1031\u102c\u1000\u103a PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a y-\u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1010\u1005\u103a\u101c\u103b\u103e\u1031\u102c\u1000\u103a MSE (\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038\u1021\u101a\u103d\u1004\u103a\u1038) \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1002\u101b\u1015\u103a\u1016\u103a\u1019\u103e\u104a PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1015\u1031\u102b\u1004\u103a\u1038\u1011\u100a\u1037\u103a\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u104f MSE \u101c\u103b\u1031\u102c\u1037\u1014\u100a\u103a\u1038\u101e\u103d\u102c\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\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u1011\u1000\u103a\u1015\u102d\u102f\u104d \u1010\u102d\u102f\u1038\u101c\u102c\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u103d\u1004\u103a \u1015\u1011\u1019 PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u101e\u102c \u1015\u102b\u101d\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\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 \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1015\u1010\u103a\u101e\u1000\u103a\u104d \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038 PLS \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u1016\u103c\u1004\u1037\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1016\u103c\u1005\u103a \u1015\u102d\u102f\u1004\u103a\u1038\u1001\u103c\u102c\u1038\u1015\u102f\u1036\u1015\u103c\u1015\u103c\u102e\u1038 \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1010\u103d\u1004\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a PLS \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f PLS \u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u1016\u103c\u1004\u1037\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span><span style=\"color: #3366ff;\">X_train<\/span> <span style=\"color: #008000;\">,<\/span> <span style=\"color: #008000;\">_<\/span><span style=\"color: #008080;\">\n\n#calculate RMSE\n<span style=\"color: #000000;\">pls = PLSRegression(n_components=2)\npls. <span style=\"color: #3366ff;\">fit<\/span> (scale(X_train), y_train)<\/span>\n\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">sqrt<\/span> (mean_squared_error(y_test, pls. <span style=\"color: #3366ff;\">predict<\/span> (scale(X_test))))\n<\/span>\n<span style=\"color: #000000;\">29.9094\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032\u104f RMSE \u101e\u100a\u103a <strong>29.9094<\/strong> \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1019\u103c\u1004\u103a\u101b\u101e\u100a\u103a\u104b \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1011\u102c\u1038\u101e\u1031\u102c <em>hp<\/em> \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u1010\u103d\u1000\u103a \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c <em>hp<\/em> \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u103c\u102c\u1038 \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u101e\u103d\u1031\u1016\u100a\u103a\u1019\u103e\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\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\/partial_least_squares.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>machine learning \u1010\u103d\u1004\u103a \u101e\u1004\u103a\u1000\u103c\u102f\u1036\u1010\u103d\u1031\u1037\u101b\u1019\u100a\u1037\u103a \u1021\u1016\u103c\u1005\u103a\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1015\u103c\u103f\u1014\u102c\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c multicollinearity \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \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 \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 \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u101c\u103d\u1014\u103a\u1006\u1000\u103a\u1005\u1015\u103a\u1014\u1031\u101e\u1031\u102c\u1021\u1001\u102b \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1016\u103c\u1005\u103a\u1015\u1031\u102b\u103a\u101e\u100a\u103a\u104b \u1011\u102d\u102f\u101e\u102d\u102f\u1037\u1016\u103c\u1005\u103a\u101c\u102c\u101e\u1031\u102c\u1021\u1001\u102b \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1000\u1031\u102c\u1004\u103a\u1038\u1005\u103d\u102c\u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e \u101e\u1031\u102c \u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1019\u1019\u103c\u1004\u103a\u1016\u1030\u1038\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1021\u101e\u1005\u103a\u1010\u103d\u1004\u103a \u100a\u1036\u1037\u1016\u103b\u1004\u103a\u1038\u1005\u103d\u102c\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1019\u100a\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u104b \u1024\u1015\u103c\u103f\u1014\u102c\u1000\u102d\u102f \u1016\u103c\u1031\u101b\u103e\u1004\u103a\u1038\u101b\u1014\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c partial least squares \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1014\u103e\u1004\u1037\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1005\u1036\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u102b\u104b \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1004\u1037\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 \u1014\u103e\u1005\u103a\u1001\u102f\u101c\u102f\u1036\u1038\u1010\u103d\u1004\u103a \u101e\u102d\u101e\u102c\u1011\u1004\u103a\u101b\u103e\u102c\u1038\u101e\u1031\u102c\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1019\u103e\u102f\u1015\u1019\u102c\u100f\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u1037\u103a p \u1019\u1030\u101c\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\u104f M linear \u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038 [&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 \u101b\u103e\u102d Partial Least Squares (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a) - Statology<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a Python \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/my\/python-\u1010\u103d\u1004\u103a-\u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038-\u1021\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u101b\u103e\u102d Partial Least Squares (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a) - Statology\" \/>\n<meta property=\"og:description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a Python \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/my\/python-\u1010\u103d\u1004\u103a-\u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038-\u1021\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T06:54:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/svppython1.png\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" 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