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Multilabel classification using a classifier ch...
"Independent" , "Chain 1" , "Chain 2" , "Chain 3" , "Chain 4" , "Chain..."Chain 5" , "Chain 6" , "Chain 7" , "Chain 8" , "Chain 9" , "Chain...scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html -
Decision boundary of semi-supervised classifier...
1 , 1 ), 0 : ( 0 , 0 , 0.9 ), 1 : ( 1 , 0 , 0 ), 2 : ( 0.8 , 0.6...x_min , x_max = X [:, 0 ] . min () - 1 , X [:, 0 ] . max () + 1...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
Pipelining: chaining a PCA and a logistic regre...
= [( "scaler" , scaler ), ( "pca" , pca ), ( "logistic" , logistic...plot ( np . arange ( 1 , pca . n_components_ + 1 ), pca . exp...scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html -
Kernel Density Estimation — scikit-learn 1.7.1 ...
new_data . reshape (( 4 , 11 , - 1 )) real_data = digits . data [:...[: 44 ] . reshape (( 4 , 11 , - 1 )) # plot real digits and resampled...scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
SVM with custom kernel — scikit-learn 1.7.1 doc...
kernel: (2 0) k(X, Y) = X ( ) Y.T (0 1) """ M = np . array ([[ 2...2 , 0 ], [ 0 , 1.0 ]]) return np . dot ( np . dot ( X , M ), Y...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
LinearSVR — scikit-learn 1.7.1 documentation
LinearSVR ( * , epsilon = 0.0 , tol = 0.0001 , C = 1.0 , loss =...becomes [x_1, ..., x_n, intercept_scaling] , i.e. a “synthetic” feature...scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html -
SimpleImputer — scikit-learn 1.7.1 documentation
transform ( X )) [[ 7. 2. 3. ] [ 4. 3.5 6. ] [10. 3.5 9. ]] For...imp_mean . fit ([[ 7 , 2 , 3 ], [ 4 , np . nan , 6 ], [ 10 , 5 , 9...scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html -
ConstantKernel — scikit-learn 1.7.1 documentation
True ) (array([606.1]), array([0.248])) __call__ ( X , Y = None...constant_value = 1.0 , constant_value_bounds = (1e-05, 100000.0) ) [source]...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html -
load_diabetes — scikit-learn 1.7.1 documentation
load_diabetes () >>> diabetes . target [: 3 ] array([151., 75., 141.]) >>>...diabetes . data . shape (442, 10) Gallery examples # Model Complexity...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html -
LeaveOneGroupOut — scikit-learn 1.7.1 documenta...
np . array ([ 1 , 2 , 1 , 2 ]) >>> groups = np . array ([ 1 , 1...enumerate ( logo . split ( X , y , groups )): ... print ( f "Fold {...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html