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RegressorChain — scikit-learn 1.7.2 documentation
2 ], [ 1 , 1 ], [ 2 , 0 ]] >>> chain = RegressorChain...predict ( X ) array([[0., 2.], [1., 1.], [2., 0.]]) fit ( X , Y ,...scikit-learn.org/stable/modules/generated/sklearn.multioutput.RegressorChain.html -
smacof — scikit-learn 1.7.2 documentation
2 ], [ 1 , 0 , 3 ], [ 2 , 3 , 0 ]]) >>> dissimilarities...metric = True , n_components = 2 , init = None , n_init = 'warn'...scikit-learn.org/stable/modules/generated/sklearn.manifold.smacof.html -
4. Metadata Routing — scikit-learn 1.7.2 docume...
2 , size = n_samples ) >>> my_groups...of its underlying objects. 4.1.2. Weighted scoring and unweighted...scikit-learn.org/stable/metadata_routing.html -
Blind source separation using FastICA — scikit-...
# Signal 2 : square signal s3 = signal . sawtooth ( 2 * np . pi...1 , 1 ], [ 0.5 , 2 , 1.0 ], [ 1.5 , 1.0 , 2.0 ]]) # Mixing matrix...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
Biclustering documents with the Spectral Co-clu...
veal bicluster 2 : 1259 documents, 3534 words categories...scikit-learn.org/stable/auto_examples/bicluster/plot_bicluster_newsgroups.html -
Compare Stochastic learning strategies for MLPC...
subplots ( 2 , 2 , figsize = ( 15 , 10 )) # load...0 , "learning_rate_init" : 0.2 , }, { "solver" : "sgd" , "learning_rate"...scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html -
Demonstration of k-means assumptions — scikit-l...
subplots ( nrows = 2 , ncols = 2 , figsize = ( 12 , 12 ))...plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12 , 12 ))...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html -
Demo of HDBSCAN clustering algorithm — scikit-l...
2 , 0.35 , 1.35 , 1.35 ], random_state...fig , axes = plt . subplots ( 2 , 1 , figsize = ( 10 , 8 )) params...scikit-learn.org/stable/auto_examples/cluster/plot_hdbscan.html -
Plot the support vectors in LinearSVC — scikit-...
centers = 2 , random_state = 0 ) plt . figure...support_vector_indices ] plt . subplot ( 1 , 2 , i + 1 ) plt . scatter ( X [:,...scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html -
check_X_y — scikit-learn 1.7.2 documentation
2 ], [ 3 , 4 ], [ 5 , 6 ]] >>> y = [ 1 , 2 , 3 ] >>>...array([[1, 2], [3, 4], [5, 6]]) >>> y array([1, 2, 3]) On this...scikit-learn.org/stable/modules/generated/sklearn.utils.check_X_y.html