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sklearn.cluster — scikit-learn 1.7.2 docu...
Popular unsupervised clustering algorithms. User guide. See the Clustering and Biclustering sections for further details.scikit-learn.org/stable/api/sklearn.cluster.html -
Introducing the set_output API — scikit-l...
798301 -1.271411 -1.327605 12 -1.244466 -0.086944 -1.327407 -1.459074...60 5.0 2.0 3.5 1.0 1 4.9 3.0 1.4 0.2 8 4.4 2.9 1.4 0.2 93 5.0 2.3...scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html -
Plot classification probability — scikit-...
GaussianProcessClass ( kernel = 1.0 * RBF ([ 1.0 , 1.0 ])), "Logistic...LogisticRegression ( C = 0.1 ), "Logistic regression \n (C=1)" : LogisticRegression...scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html -
Decision Tree Regression — scikit-learn 1...
RandomState ( 1 ) X = np . sort ( 5 * rng . rand ( 80 , 1 ), axis =...RandomState ( 1 ) X = np . sort ( 200 * rng . rand ( 100 , 1 ) - 100...scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html -
SVM: Weighted samples — scikit-learn 1.7....
n_clusters_per_class = 1 , class_sep = 1.1 , weights = [ 0.9 , 0.1 ], random_state...( X_plot [:, 0 ], X_plot [:, 1 ], c = y_plot , s = 100 * sample_weight...scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html -
Underfitting vs. Overfitting — scikit-lea...
subplot ( 1 , len ( degrees ), i + 1 ) plt . setp ( ax...function (polynomial with degree 1) is not sufficient to fit the...scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html -
FeatureHasher and DictVectorizer Comparison ...
'example': 1, 'but': 1, 'this': 1, 'another':...tokenize ( doc ): freq [ tok ] += 1 return freq token_freqs ( "That...scikit-learn.org/stable/auto_examples/text/plot_hashing_vs_dict_vectorizer.html -
Visualizations with Display Objects — sci...
C 1.0 fit_intercept True intercept_scaling 1 class_weight ...C 1.0 fit_intercept True intercept_scaling 1 class_weight ...scikit-learn.org/stable/auto_examples/miscellaneous/plot_display_object_visualization.html -
RBF SVM parameters — scikit-learn 1.7.2 d...
1 , 1e2 ] gamma_2d_range = [ 1e-1 , 1 , 1e1 ] classifiers...np.float64(1.0), 'gamma': np.float64(0.1)} with a score...scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html -
4. Metadata Routing — scikit-learn 1.7.2 ...
1.1. Weighted scoring and fitting...param_grid = { "C" : [ 0.1 , 1 ]} >>> lr = LogisticRegression...scikit-learn.org/stable/metadata_routing.html