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Clustering text documents using k-means — sciki...
assigned to each cluster: [ 1 1 1 3384] Number of elements assigned...n_features: { X_tfidf . shape [ 1 ] } " ) vectorization done in...scikit-learn.org/stable/auto_examples/text/plot_document_clustering.html -
weighted_mode — scikit-learn 1.7.0 documentation
1 , 4 , 2 , 4 , 2 ] >>> weights = [ 1 , 1 , 1 , 1 , 1 , 1...distribution. >>> weights = [ 1 , 3 , 0.5 , 1.5 , 1 , 2 ] # deweight the...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.weighted_mode.html -
make_friedman3 — scikit-learn 1.7.0 documentation
1 ] <= 560 * pi , 0 <= X [:, 2 ] <= 1 , 1 <= X [:, 3...arctan (( X [:, 1 ] * X [:, 2 ] - 1 / ( X [:, 1 ] * X [:, 3 ]))...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html -
calibration_curve — scikit-learn 1.7.0 document...
1 , 1 , 1 , 1 , 1 ]) >>> y_pred = np . array ([ 0.1 , 0.2...positive class. Added in version 1.1. n_bins int, default=5 Number...scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html -
Model selection with Probabilistic PCA and Fact...
5 sigma = 1.0 rng = np . random . RandomState...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
Single estimator versus bagging: bias-variance ...
n_estimators - 1 : plt . legend ( loc = ( 1.1 , 0.5 )) plt ....axis = 1 )) ** 2 y_var = np . var ( y_predict , axis = 1 ) print...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html -
get_scorer — scikit-learn 1.7.0 documentation
1 , - 1 , - 0.5 , 2 ], ( - 1 , 1 )) >>> y = np...np . array ([ 0 , 1 , 1 , 0 , 1 ]) >>> classifier = DummyClassifier...scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer.html -
root_mean_squared_log_error — scikit-learn 1.7....
Added in version 1.4. Parameters : y_true array-like...scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_log_error.html -
sigmoid_kernel — scikit-learn 1.7.0 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...defaults to 1.0 / n_features. coef0 float, default=1 Constant offset...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html -
ndcg_score — scikit-learn 1.7.0 documentation
1.1 , 1. , .5 , .0 ]]) >>> ndcg_score...scores = np . asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html