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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 -
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 -
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 -
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 -
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 -
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 -
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 -
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 -
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 -
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