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SVM: Separating hyperplane for unbalanced class...
0 ]] clusters_std = [ 1.5 , 0.5 ] X , y = make_blobs (...kernel = "linear" , C = 1.0 ) clf . fit ( X , y ) # fit...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
GaussianProcessClassifier — scikit-learn ...
1, 3.2, and 5.1 from [RW2006] . Internally,...None is passed, the kernel “1.0 * RBF(1.0)” is used as default. Note...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html -
7.3. Preprocessing data — scikit-learn 1....
1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1], ...,...0. , -1.22, 1.33 ], [ 1.22, 0. , -0.267], [-1.22, 1.22, -1.06 ]])...scikit-learn.org/stable/modules/preprocessing.html -
Joint feature selection with multi-task Lasso &...
ylim ([ - 1.1 , 1.1 ]) plt . show () Total running...(( 1.0 + rng . randn ( 1 )) * times + 3 * rng . randn ( 1 ))...scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html -
Exponentiation — scikit-learn 1.8.0 docum...
predict ( X [: 1 ,:], return_std = True ) (array([635.5]),...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html -
Importance of Feature Scaling — scikit-le...
it has a standard deviation of 1 and a mean of 0. Even if tree...variable “hue” varies between 1 and 10. Because of this, distances...scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html -
Faces recognition example using eigenfaces and ...
1 )[ - 1 ] true_name = target_names.... rsplit ( " " , 1 )[ - 1 ] return "predicted:...scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html -
7.2. Feature extraction — scikit-learn 1....
array([[1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1]]) In...array([[0, 1, 1, 1, 0, 0, 1, 0, 1], [0, 1, 0, 1, 0, 2, 1, 0, 1], [1,...scikit-learn.org/stable/modules/feature_extraction.html -
Demo of OPTICS clustering algorithm — sci...
subplot ( G [ 1 , 0 ]) ax3 = plt . subplot ( G [ 1 , 1 ]) ax4 = plt...labels_ == - 1 , 0 ], X [ clust . labels_ == - 1 , 1 ], "k+"...scikit-learn.org/stable/auto_examples/cluster/plot_optics.html -
SGD: convex loss functions — scikit-learn...
>= - 1 ] = ( 1 - z [ z >= - 1 ]) ** 2 loss [ z >= 1.0 ]...([ xmin , 0 , 0 , xmax ], [ 1 , 1 , 0 , 0 ], color = "gold"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html