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calibration_curve — scikit-learn 1.8.0 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 -
NuSVC — scikit-learn 1.8.0 documentation
array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...default=-1 Hard limit on iterations within solver, or -1 for no...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html -
get_scorer — scikit-learn 1.8.0 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 -
dict_learning_online — scikit-learn 1.8.0 docum...
1 ( U , V ) with || V_k || _2 = 1 for all 0 <= k...heuristics. Added in version 1.1. return_code bool, default=True...scikit-learn.org/stable/modules/generated/sklearn.decomposition.dict_learning_online.html -
KFold — scikit-learn 1.8.0 documentation
3] Test: index=[0 1] Fold 1: Train: index=[0 1] Test: index=[2...X = np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html -
linear_kernel — scikit-learn 1.8.0 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...linear_kernel ( X , Y ) array([[0., 0.], [1., 2.]]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html -
An example of K-Means++ initialization — scikit...
:: - 1 ] # Calculate seeds from k-means++...side sample data plt . figure ( 1 ) colors = [ "#4EACC5" , "#FF9C34"...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html -
LeaveOneGroupOut — scikit-learn 1.8.0 documenta...
index=[0 1], group=[1 1] Fold 1: Train: index=[0 1], group=[1 1] Test:...array ([ 1 , 2 , 1 , 2 ]) >>> groups = np . array ([ 1 , 1 , 2 ,...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html -
dcg_score — scikit-learn 1.8.0 documentation
asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...to have a score between 0 and 1. References Wikipedia entry for...scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html -
ElasticNetCV — scikit-learn 1.8.0 documentation
l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the penalty...(i.e. Ridge), as in [.1, .5, .7, .9, .95, .99, 1] . eps float, default=1e-3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html