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SelectFromModel — scikit-learn 1.5.2 documentation
[ - 2.79 , - 0.02 , - 0.85 ], ... [ - 1.34 , - 0.48 , - 2.55 ],...the estimator is of dimension 2. max_features int, callable, default=None...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
rand_score — scikit-learn 1.5.2 documentation
of Classification 2, 193–218 (1985). . [ 2 ] Wikipedia: Simple...predicted and true clusterings [1] [2] . The raw RI score [3] is: RI...scikit-learn.org/stable/modules/generated/sklearn.metrics.rand_score.html -
adjusted_rand_score — scikit-learn 1.5.2 docume...
2 ], [ 0 , 0 , 1 , 1 ]) 0.57......, 0 , 1 , 1 ], [ 0 , 0 , 1 , 2 ]) 0.57... If classes members...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
RFECV — scikit-learn 1.5.2 documentation
2, 5]) property classes_ # Classes...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html -
1.5. Stochastic Gradient Descent — scikit-learn...
:= \frac{1}{2} \sum_{j=1}^{m} w_j^2 = ||w||_2^2\) , L1 norm:...values: >>> clf . predict ([[ 2. , 2. ]]) array([1]) SGD fits a...scikit-learn.org/stable/modules/sgd.html -
make_multilabel_classification — scikit-learn 1...
n_labels = 2 , length = 50 , allow_unlabeled...problem. n_labels int, default=2 The average number of labels per...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_multilabel_classification.html -
load_diabetes — scikit-learn 1.5.2 documentation
2 < x < .2 Targets integer 25 - 346...scikit-learn 1.2 Release Highlights for scikit-learn 1.2 Gradient Boosting...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html -
TfidfVectorizer — scikit-learn 1.5.2 documentation
2) means unigrams and bigrams, and (2, 2) means only...default regexp selects tokens of 2 or more alphanumeric characters...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html -
Demo of OPTICS clustering algorithm — scikit-le...
2 ) C3 = [ 1 , - 2 ] + 0.2 * np . random . randn...n_points_per_cluster , 2 ) C4 = [ - 2 , 3 ] + 0.3 * np . random...scikit-learn.org/stable/auto_examples/cluster/plot_optics.html -
f_classif — scikit-learn 1.5.2 documentation
2.2...e-02, 5.7...e-01, 8.2...e-01, 4.5...e-01,..., y ) >>> f_statistic array([2.2...e+02, 7.0...e-01, 1.6...e+00,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_classif.html