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  1. GradientBoostingRegressor — scikit-learn 1.7.2 ...

    n_estimators = 100 , subsample = 1.0 , criterion = 'friedman_mse' ,...min_weight_fraction_leaf = 0.0 , max_depth = 3 , min_impurity_decrease = 0.0 , init...
    scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
    Sat Oct 11 07:51:26 UTC 2025
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  2. LatentDirichletAllocation — scikit-learn 1.7.2 ...

    max_doc_update_iter = 100 , n_jobs = None , verbose = 0 , random_state = None...n_components = 10 , * , doc_topic_prior = None , topic_word_prior = None...
    scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
    Sat Oct 11 07:51:26 UTC 2025
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  3. Lasso model selection: AIC-BIC / cross-validati...

    y = load_diabetes ( return_X_y = True , as_frame = True )...x_min = x . min () return [ "font-weight: bold" if v == x_min...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html
    Sat Oct 11 07:51:25 UTC 2025
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  4. precision_recall_fscore_support — scikit-learn ...

    beta = 1.0 , labels = None , pos_label = 1 , average = None ,...warn_for = ('precision', 'recall', 'f-score') , sample_weight = None...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
    Sat Oct 11 07:51:26 UTC 2025
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  5. make_regression — scikit-learn 1.7.2 documentation

    tail_strength = 0.5 , noise = 0.0 , shuffle = True , coef = False , random_state...n_samples = 100 , n_features = 100 , * , n_informative = 10 , n_targets...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html
    Sat Oct 11 07:51:26 UTC 2025
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  6. Gaussian Mixture Model Selection — scikit-learn...

    ( X [ Y_ == i , 0 ], X [ Y_ == i , 1 ], 0.8 , color = color )...data = df , kind = "bar" , x = "Number of components" , y = "BIC...
    scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html
    Sat Oct 11 07:51:26 UTC 2025
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  7. Multilabel classification using a classifier ch...

    ) Y = Y == "TRUE" X_train , X_test , Y_train , Y_test = train_test_split..., Y = fetch_openml ( "yeast" , version = 4 , return_X_y = True...
    scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html
    Sat Oct 11 07:51:26 UTC 2025
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  8. 定まったリモートAPI呼び出し (RemoteApi Call) | LastaFlute

    LastaRemoteBehavior { // ========== // Constructor // ========== public Rem...requestManager ); } // ========== // Initialize // ========== @Override protected...
    dbflute.seasar.org/ja/lastaflute/howto/architecture/remoteapicall.html
    Mon Sep 15 10:51:17 UTC 2025
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  9. Column Transformer with Heterogeneous Data Sour...

    random_state = 1 , subset = "train" , categories = categories ,...y_test = fetch_20newsgroups ( random_state = 1 , subset = "test"...
    scikit-learn.org/stable/auto_examples/compose/plot_column_transformer.html
    Sat Oct 11 07:51:25 UTC 2025
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  10. Recursive feature elimination with cross-valida...

    n_informative = 3 , n_redundant = 2 , n_repeated = 0 , n_classes = 8 ,..., y = make_classification ( n_samples = 500 , n_features = n_features...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html
    Sat Oct 11 07:51:26 UTC 2025
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