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  1. MiniBatchNMF — scikit-learn 1.6.1 documentation

    max_iter = 200 , alpha_W = 0.0 , alpha_H = 'same' , l1_ratio = 0.0...n_components = 'auto' , * , init = None , batch_size = 1024 , beta_loss...
    scikit-learn.org/stable/modules/generated/sklearn.decomposition.MiniBatchNMF.html
    Sat Apr 19 00:31:21 UTC 2025
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  2. dcg_score — scikit-learn 1.6.1 documentation

    k = None , log_base = 2 , sample_weight = None , ignore_ties.../ 2 = 7.5 >>> dcg_score ( true_relevance , scores , k = 1 ) np.float64(7.5)...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html
    Sat Apr 19 00:31:22 UTC 2025
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  3. d2_pinball_score — scikit-learn 1.6.1 documenta...

    sample_weight = None , alpha = 0.5 , multioutput = 'uniform_average'...(n_samples,), default=None Sample weights. alpha float, default=0.5 Slope...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_pinball_score.html
    Sat Apr 19 00:31:21 UTC 2025
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  4. pairwise_kernels — scikit-learn 1.6.1 documenta...

    Y = None , metric = 'linear' , * , filter_params = False...(n_samples_X, n_samples_X) if metric == “precomputed” and (n_samples_X,...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
    Sat Apr 19 00:31:21 UTC 2025
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  5. StratifiedGroupKFold — scikit-learn 1.6.1 docum...

    ( n_splits = 5 , shuffle = False , random_state = None ) [source]...StratifiedGroupKFold(n_splits=3, random_state=None, shuffle=False) >>> for i...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html
    Sat Apr 19 00:31:22 UTC 2025
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  6. HuberRegressor vs Ridge on dataset with strong ...

    n_samples = 20 , n_features = 1 , random_state = 0 , noise = 4.0 ,...): huber = HuberRegressor ( alpha = 0.0 , epsilon = epsilon )...
    scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html
    Sat Apr 19 00:31:22 UTC 2025
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  7. Plot individual and voting regression predictio...

    ( axis = "x" , which = "both" , bottom = False , top = False ,...reg1 = GradientBoostingRegr ( random_state = 1 ) reg2 = RandomForestRegresso...
    scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html
    Sat Apr 19 00:31:22 UTC 2025
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  8. enet_path — scikit-learn 1.6.1 documentation

    l1_ratio = 0.5 , eps = 0.001 , n_alphas = 100 , alphas = None ,...precompute = 'auto' , Xy = None , copy_X = True , coef_init = None...
    scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html
    Sat Apr 19 00:31:21 UTC 2025
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  9. DecisionBoundaryDisplay — scikit-learn 1.6.1 do...

    plot_method = 'contourf' , ax = None , xlabel = None , ylabel = None...display = DecisionBoundaryDisp ( ... xx0 = feature_1 , xx1 = feature_2...
    scikit-learn.org/stable/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html
    Sat Apr 19 00:31:22 UTC 2025
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  10. LocallyLinearEmbedding — scikit-learn 1.6.1 doc...

    n_neighbors = 5 , n_components = 2 , reg = 0.001 , eigen_solver = 'auto'...'auto' , tol = 1e-06 , max_iter = 100 , method = 'standard' , hessian_tol...
    scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
    Sat Apr 19 00:31:21 UTC 2025
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