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make_circles — scikit-learn 1.7.2 documen...
int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(0)]...outer circle in the range [0, 1) . Returns : X ndarray of shape...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html -
BayesianGaussianMixture — scikit-learn 1....
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], [ 4 , 2 ], [...mixtures”. Bayesian analysis 1.1 Examples >>> import...scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html -
NMF — scikit-learn 1.7.2 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...version 1.4: Added 'auto' value. Changed in version 1.6: Default...scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html -
cluster_optics_dbscan — scikit-learn 1.7....
1, 1, 1]) Gallery examples # Demo of...>>> X = np . array ([[ 1 , 2 ], [ 2 , 5 ], [ 3 , 6 ], ......scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_dbscan.html -
ElasticNetCV — scikit-learn 1.7.2 documen...
l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the...(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 -
RobustScaler — scikit-learn 1.7.2 documen...
[[ 1. , - 2. , 2. ], ... [ - 2. , 1. , 3. ], ... [ 4. , 1. ,..., -2. , 0. ], [-1. , 0. , 0.4], [ 1. , 0. , -1.6]]) fit ( X ,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html -
AgglomerativeClustering — scikit-learn 1....
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...clustering . labels_ array([1, 1, 1, 0, 0, 0]) For a comparison...scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html -
OutputCodeClassifier — scikit-learn 1.7.2...
means 1 unless in a joblib.parallel_backend context. -1 means...( estimator , * , code_size = 1.5 , random_state = None , n_jobs...scikit-learn.org/stable/modules/generated/sklearn.multiclass.OutputCodeClassifier.html -
Lasso — scikit-learn 1.7.2 documentation
1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1 ], [ 2...2 , 2 ]], [ 0 , 1 , 2 ]) Lasso(alpha=0.1) >>> print (...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html -
LassoLarsIC — scikit-learn 1.7.2 document...
[ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]] >>>...fit_intercept . Added in version 1.1. Attributes : coef_ array-like...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html