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Demo of OPTICS clustering algorithm — scikit-le...
subplot ( G [ 1 , 0 ]) ax3 = plt . subplot ( G [ 1 , 1 ]) ax4 = plt...labels_ == - 1 , 0 ], X [ clust . labels_ == - 1 , 1 ], "k+" , alpha...scikit-learn.org/stable/auto_examples/cluster/plot_optics.html -
Tweedie regression on insurance claims — scikit...
tweedie_powers = [ 1.5 , 1.7 , 1.8 , 1.9 , 1.99 , 1.999 , 1.9999 ] scores_product_model...dev p=1.9990 1.914574e+03 1.914370e+03 1.914537e+03 1.914388e+03...scikit-learn.org/stable/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html -
check_symmetric — scikit-learn 1.7.1 documentation
1 , 2 ], [ 1 , 0 , 1 ], [ 2 , 1 , 0 ]]) >>> check_symmetric...symmetric_array ) array([[0, 1, 2], [1, 0, 1], [2, 1, 0]]) >>> from scipy.sparse...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_symmetric.html -
Wikipedia principal eigenvector — scikit-learn ...
1 )[ 1 ] page_links_url = "http:/...page_links_url . rsplit ( "/" , 1 )[ 1 ] resources = [ ( redirects_url...scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html -
2. Unsupervised learning — scikit-learn 1.7.1 d...
1. Gaussian mixture models 2.1.1. Gaussian Mixture 2.1.2....Mixture 2.2. Manifold learning 2.2.1. Introduction 2.2.2. Isomap 2.2.3....scikit-learn.org/stable/unsupervised_learning.html -
8.2. Real world datasets — scikit-learn 1.7.1 d...
1, 1, 1, 0, 1, 1, 0, 0, 0]) Converting...target [: 10 ]) [5, 6, 3, 1, 0, 1, 3, 4, 3, 0] The second loader...scikit-learn.org/stable/datasets/real_world.html -
Target Encoder’s Internal Cross fitting — sciki...
reshape ( - 1 , 1 )) # Remove the linear relationship...replace = True ) . reshape ( - 1 , 1 ) Finally, we assemble the dataset...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Demonstrating the different strategies of KBins...
]]) centers_1 = np . array ([[ 0 , 0 ], [ 3 , 1 ]]) # construct...strategies ) + 1 , i ) ax . scatter ( X [:, 0 ], X [:, 1 ], edgecolors...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html -
dict_learning — scikit-learn 1.7.1 documentation
* || U || _1 , 1 ( U , V ) with || V_k || _2 = 1 for all 0 <=...the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix...scikit-learn.org/stable/modules/generated/sklearn.decomposition.dict_learning.html -
enet_path — scikit-learn 1.7.1 documentation
l1_ratio * || w || _1 + 0.5 * alpha * ( 1 - l1_ratio ) * || w...0.568], [ 0., 1.120, 0.620], [-0., -2.129, -1.128], [ 0., 23.046,...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html