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  1. grid_to_graph — scikit-learn 1.7.0 documentation

    n_z=1 , * , mask=None , return_as=<class 'scipy.s...shape_img = ( 4 , 4 , 1 ) >>> mask = np . zeros ( shape = shape_img...
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html
    Tue Jul 01 15:59:34 UTC 2025
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  2. StratifiedKFold — scikit-learn 1.7.0 documentation

    n_splits = 5 , * , shuffle = False , random_state = None ) [source]...StratifiedKFold(n_splits=2, random_state=None, shuffle=False) >>> for i...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html
    Tue Jul 01 15:59:32 UTC 2025
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  3. DictVectorizer — scikit-learn 1.7.0 documentation

    dtype=<class 'numpy.float64'> , separator='=' , sparse=True ,...DictVectorizer >>> v = DictVectorizer ( sparse = False ) >>> D = [{ 'foo'...
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html
    Tue Jul 01 15:59:33 UTC 2025
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  4. ExtraTreeClassifier — scikit-learn 1.7.0 docume...

    criterion = 'gini' , splitter = 'random' , max_depth = None , min_samples_split...min_weight_fraction_leaf = 0.0 , max_features = 'sqrt' , random_state = None ,...
    scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html
    Mon Jun 30 13:50:28 UTC 2025
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  5. ConstantKernel — scikit-learn 1.7.0 documentation

    y = make_friedman2 ( n_samples = 500 , noise = 0 , random_state...constant_value = 2 ) >>> gpr = GaussianProcessRegre ( kernel = kernel...
    scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html
    Tue Jul 01 15:59:32 UTC 2025
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  6. cross_val_score — scikit-learn 1.7.0 documentation

    y = None , * , groups = None , scoring = None , cv = None...n_jobs = None , verbose = 0 , params = None , pre_dispatch = '2*n_jobs'...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html
    Tue Jul 01 15:59:32 UTC 2025
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  7. make_friedman3 — scikit-learn 1.7.0 documentation

    intervals: 0 <= X [:, 0 ] <= 100 , 40 * pi <= X [:, 1 ] <= 560 * pi...pi , 0 <= X [:, 2 ] <= 1 , 1 <= X [:, 3 ] <= 11. The output y...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html
    Tue Jul 01 15:59:32 UTC 2025
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  8. RandomForestRegressor — scikit-learn 1.7.0 docu...

    n_estimators = 100 , * , criterion = 'squared_error' , max_depth = None...min_weight_fraction_leaf = 0.0 , max_features = 1.0 , max_leaf_nodes = None , ...
    scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
    Tue Jul 01 15:59:32 UTC 2025
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  9. 名前付き区分値の自動生成(namedcls) | LastaFlute

    map:{ code=[code]; name=[name]; alias=[alias]; comment=[comment]...map:{ code=[code]; name=[name]; alias=[alias]; comment=[comment]...
    dbflute.seasar.org/ja/lastaflute/howto/dbflute/namedcls.html
    Fri Jun 13 09:55:28 UTC 2025
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  10. A demo of structured Ward hierarchical clusteri...

    contour ( label == l , colors = [ plt . cm . nipy_spectral...smoothened_coins = gaussian_filter ( orig_coins , sigma = 2 ) rescaled_coins...
    scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html
    Tue Jul 01 15:59:35 UTC 2025
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