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Version 1.0 — scikit-learn 1.7.2 document...
Version 1.0.1 # October 2021 Fixed models...Version 1.0.0 of scikit-learn requires python 3.7+, numpy 1.14.6+...scikit-learn.org/stable/whats_new/v1.0.html -
Version 1.2 — scikit-learn 1.7.2 document...
is deprecated in 1.2.1 and will be removed in 1.5. Instead, import...raised in version 1.2 when min_sample_split=1 . #25744 by Jérémie...scikit-learn.org/stable/whats_new/v1.2.html -
9.1. Strategies to scale computationally: bigge...
1.1.1. Streaming instances # Basically, 1. may be a...to make your system scale. 9.1.1. Scaling with instances using...scikit-learn.org/stable/computing/scaling_strategies.html -
2.1. Gaussian mixture models — scikit-lea...
1.1. Gaussian Mixture # The GaussianMixture...models using a finite mixture. 2.1.2.1. The Dirichlet Process # Here...scikit-learn.org/stable/modules/mixture.html -
CountVectorizer — scikit-learn 1.8.0 docu...
[[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0...[[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html -
completeness_score — scikit-learn 1.8.0 d...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
brier_score_loss — scikit-learn 1.8.0 doc...
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...defined as: \[\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}(y_{ic} - \hat{p}_{ic})^{2}\]...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
sparse_encode — scikit-learn 1.8.0 docume...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.sparse_encode.html -
polynomial_kernel — scikit-learn 1.8.0 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0..., degree = 2 ) array([[1. , 1. ], [1.77, 2.77]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html -
paired_manhattan_distances — scikit-learn...
array ([[ 1 , 1 , 0 ], [ 0 , 1 , 0 ], [ 0 , 0 , 1 ]]) >>>...calculated between (X[0], Y[0]), (X[1], Y[1]), …, (X[n_samples], Y[n_samples])....scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html