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LassoLars — scikit-learn 1.8.0 documentation
([[ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ]], [ - 1 , 0 , - 1 ]) LassoLars(alpha=0.01)...sklearn.linear_model. LassoLars ( alpha = 1.0 , * , fit_intercept = True ,...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html -
IsotonicRegression — scikit-learn 1.8.0 documen...
1 , .2 ]) array([1.8628, 3.7256]) fit (...n_samples = 10 , n_features = 1 , random_state = 41 ) >>> iso_reg...scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html -
MLPRegressor — scikit-learn 1.8.0 documentation
returns f(x) = 1 / (1 + exp(-x)). ‘tanh’, the hyperbolic...random_state = 1 ) >>> regr = MLPRegressor ( random_state = 1 , max_iter...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html -
make_circles — scikit-learn 1.8.0 documentation
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 -
HDBSCAN — scikit-learn 1.8.0 documentation
means 1 unless in a joblib.parallel_backend context. -1 means.... labels_ ) . tolist () [-1, 0, 1, 2, 3, 4, 5, 6, 7] dbscan_clustering...scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html -
MinMaxScaler — scikit-learn 1.8.0 documentation
data = [[ - 1 , 2 ], [ - 0.5 , 6 ], [ 0 , 10 ], [ 1 , 18 ]] >>>...0. ] [0.25 0.25] [0.5 0.5 ] [1. 1. ]] >>> print ( scaler . transform...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html -
Isotonic Regression — scikit-learn 1.8.0 docume...
versionadded:: 1.7 1e-06 n_jobs n_jobs: int, default=None...that is if firstly `n_targets > 1` and secondly `X` is sparse or...scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html -
make_swiss_roll — scikit-learn 1.8.0 documentation
is from Marsland [1] . References [ 1 ] ( 1 , 2 ) S. Marsland,...from Stephen Marsland’s code [1] . Parameters : n_samples int,...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html -
zero_one_loss — scikit-learn 1.8.0 documentation
1 ], [ 1 , 1 ]]), np . ones (( 2 , 2...zero_one_loss >>> y_pred = [ 1 , 2 , 3 , 4 ] >>> y_true = [ 2...scikit-learn.org/stable/modules/generated/sklearn.metrics.zero_one_loss.html -
FeatureUnion — scikit-learn 1.8.0 documentation
svd__n_components = 1 ) . fit_transform ( X ) array([[-1.5 , 3.04], [ 1.5 , 5.72]])...unchanged. Added in version 1.1: Added the option "passthrough"...scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html