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  1. DummyRegressor — scikit-learn 1.7.0 documentation

    strategy = 'mean' , constant = None , quantile = None ) [source]...>>> X = np . array ([ 1.0 , 2.0 , 3.0 , 4.0 ]) >>> y = np . array...
    scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html
    Tue Jul 01 15:59:32 UTC 2025
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  2. HistGradientBoostingClassifier — scikit-learn 1...

    n_iter_no_change = 10 , tol = 1e-07 , verbose = 0 , random_state = None ,...loss = 'log_loss' , * , learning_rate = 0.1 , max_iter = 100 ,...
    scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
    Tue Jul 01 15:59:32 UTC 2025
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  3. Early stopping in Gradient Boosting — scikit-le...

    params = dict ( n_estimators = 1000 , max_depth = 5 , learning_rate...learning_rate = 0.1 , random_state = 42 ) gbm_full = GradientBoostingRegr...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html
    Tue Jul 01 15:59:35 UTC 2025
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  4. LassoLarsIC — scikit-learn 1.7.0 documentation

    criterion = 'aic' , * , fit_intercept = True , verbose = False ,..., precompute = 'auto' , max_iter = 500 , eps = np.float64(2....
    scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html
    Tue Jul 01 15:59:34 UTC 2025
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  5. Lars — scikit-learn 1.7.0 documentation

    fit_intercept = True , verbose = False , precompute = 'auto' , n_nonzero_coefs...6) , copy_X = True , fit_path = True , jitter = None , random_state...
    scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html
    Tue Jul 01 15:59:32 UTC 2025
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  6. Ordinary Least Squares and Ridge Regression Var...

    s = 30 , c = "red" , marker = "+" , zorder = 10 ) ax ....c = "gray" , marker = "o" , zorder = 10 ) clf . fit ( X_train...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html
    Thu Jun 05 00:23:05 UTC 2025
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  7. Orthogonal Matching Pursuit — scikit-learn 1.7....

    data # y = Xw # |x|_0 = n_nonzero_coefs y , X , w = make_sparse_coded_signal...n_samples = 1 , n_components = n_components , n_features = n_features...
    scikit-learn.org/stable/auto_examples/linear_model/plot_omp.html
    Tue Jul 01 15:59:33 UTC 2025
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  8. make_blobs — scikit-learn 1.7.0 documentation

    ( n_samples = 100 , n_features = 2 , * , centers = None , cluster_std...cluster_std = 1.0 , center_box = (-10.0, 10.0) , shuffle = True ,...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html
    Tue Jul 01 15:59:32 UTC 2025
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  9. FeatureUnion — scikit-learn 1.7.0 documentation

    n_jobs = None , transformer_weights = None , verbose = False ,...union = FeatureUnion ([( "pca" , PCA ( n_components = 1 )), ......
    scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html
    Tue Jul 01 15:59:32 UTC 2025
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  10. GaussianProcessClassifier — scikit-learn 1.7.0 ...

    n_restarts_optimizer = 0 , max_iter_predict = 100 , warm_start = False , copy_X_train...copy_X_train = True , random_state = None , multi_class = 'one_vs_rest'...
    scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
    Tue Jul 01 15:59:32 UTC 2025
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