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Plot Hierarchical Clustering Dendrogram — sciki...
dendrogram ( linkage_matrix , ** kwargs ) iris = load_iris () X = iris...plot_dendrogram ( model , truncate_mode = "level" , p = 3 ) plt . xlabel...scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html -
Image denoising using dictionary learning — sci...
, size = 16 ) plt . subplots_adjust ( 0.02 , 0.02 , 0.98 , 0.79...= "nearest" ) plt . xticks (()) plt . yticks (()) plt . subplot...scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html -
Post pruning decision trees with cost complexit...
alpha for training set" ) Text(0.5, 1.0, 'Total Impurity vs effective...= "steps-post" ) ax . set_xlabel ( "effective alpha" ) ax . set_ylabel...scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html -
Comparison of F-test and mutual information — s...
follows: y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the...* np . pi * X [:, 1 ]) + 0.1 * np . random . randn ( 1000 ) f_test...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
Plot classification boundaries with different S...
0.4 ], [ - 0.5 , 1.2 ], [ - 1.5 , 2.1 ], [ 1.0 , 1.0 ], [ 1.3...array ([ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 1 , 1 , 1 , 1 , 1 , 1 ,...scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html -
RBF SVM parameters — scikit-learn 1.7.1 documen...
parameters are {'C': np.float64(1.0), 'gamma': np.float64(0.1)} with a...self . midpoint , self . vmax ], [ 0 , 0.5 , 1 ] return np . ma...scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html -
SVM: Separating hyperplane for unbalanced class...
n_samples_2 = 100 centers = [[ 0.0 , 0.0 ], [ 2.0 , 2.0 ]] clusters_std...svm . SVC ( kernel = "linear" , C = 1.0 ) clf . fit ( X , y )...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
SVM: Weighted samples — scikit-learn 1.7.1 docu...
weights = [ 0.9 , 0.1 ], random_state = 0 , ) # down-sample for plotting...plot.""" axis . scatter ( X_plot [:, 0 ], X_plot [:, 1 ], c =...scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html -
Selecting dimensionality reduction with Pipelin...
', MinMaxScaler()), ('reduce_dim', 'passthrough'), ('classify',...{'classify__C': [1, 10, ...], 'reduce_dim': [SelectKBest(s...7f48a4c1dfc0>)],...scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html -
Plot the support vectors in LinearSVC — scikit-...
linestyles = [ "--" , "-" , "--" ], ) plt . scatter ( support_vectors...figsize = ( 10 , 5 )) for i , C in enumerate ([ 1 , 100 ]): # "hinge"...scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html