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7.3. Preprocessing data — scikit-learn 1.8.0 do...
array([[1., 0., 0., 1., 0., 1.], [0., 1., 1., 0., 0., 1.]]) By...2. , 1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1], ...,...scikit-learn.org/stable/modules/preprocessing.html -
Comparing different hierarchical linkage method...
right = 0.98 , bottom = 0.001 , top = 0.96 , wspace = 0.05 , hspace...transformation = [[ 0.6 , - 0.6 ], [ - 0.4 , 0.8 ]] X_aniso = np...scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html -
Demo of OPTICS clustering algorithm — scikit-le...
== - 1 , 0 ], X [ labels_050 == - 1 , 1 ], "k+" , alpha = 0.1 )...== - 1 , 0 ], X [ labels_200 == - 1 , 1 ], "k+" , alpha = 0.1 )...scikit-learn.org/stable/auto_examples/cluster/plot_optics.html -
Faces dataset decompositions — scikit-learn 1.8...
w_pad = 0.01 , h_pad = 0.02 , hspace = 0 , wspace = 0 ) fig ....Duality gap: 7.629e-06, tolerance: 1.014e-06 /home/circleci/pr...scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html -
Ledoit-Wolf vs OAS estimation — scikit-learn 1....
ylim ()[ 0 ], 1.0 + ( plt . ylim ()[ 1 ] - plt . ylim ()[ 0 ]) /...covariance matrix (AR(1) process) r = 0.1 real_cov = toeplitz ( r **...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html -
Normal, Ledoit-Wolf and OAS Linear Discriminant...
score_clf3 = 0 , 0 , 0 for _ in range ( n_averages..."lower left" ) plt . ylim (( 0.65 , 1.0 )) plt . suptitle ( "LDA...scikit-learn.org/stable/auto_examples/classification/plot_lda.html -
A demo of the mean-shift clustering algorithm —...
centers = [[ 1 , 1 ], [ - 1 , - 1 ], [ 1 , - 1 ]] X , _ = make_blobs...n_samples = 10000 , centers = centers , cluster_std = 0.6 ) Compute...scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html -
Bisecting K-Means and Regular K-Means Performan...
0 ], X [:, 1 ], s = 10 , c = algo . labels_...running time of the script: (0 minutes 1.091 seconds) Download Jupyter...scikit-learn.org/stable/auto_examples/cluster/plot_bisect_kmeans.html -
Categorical Feature Support in Gradient Boostin...
`interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2,...`interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2,...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_categorical.html -
Recursive feature elimination — scikit-learn 1....
n_features_to_select = 1 , step = 1 )), ] ) pipe . fit ( X ,.... shape [ 0 ]): for j in range ( ranking . shape [ 1 ]): plt ....scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html