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StratifiedKFold — scikit-learn 1.6.1 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 -
LeaveOneGroupOut — scikit-learn 1.6.1 documenta...
Train: index=[2 3], group=[2 2] Test: index=[0 1], group=[1 1] Fold...Train: index=[0 1], group=[1 1] Test: index=[2 3], group=[2 2] get_metadata_routing...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html -
BaggingClassifier — scikit-learn 1.6.1 document...
special cases with k == 1 , otherwise k==n_classes . property...estimator = None , n_estimators = 10 , * , max_samples = 1.0 , max_features...scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html -
Actionの実装デザイン | LastaFlute
HarborBaseAction { // ========== // Attribute // ========= @Resource private...purchaseBhv ; // ========== // Execute // ======= @Execute public...dbflute.seasar.org/ja/lastaflute/howto/impldesign/actiondesign.html -
TargetTags — scikit-learn 1.6.1 documentation
one_d_labels : bool = False , two_d_labels : bool = False , positive_only...positive_only : bool = False , multi_output : bool = False , single_output...scikit-learn.org/stable/modules/generated/sklearn.utils.TargetTags.html -
genba-implfit-Classification-is-completion.png
35273367 width=759, height=191, bitDepth=8, colorType=RGB, compr...compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none RGB...dbflute.seasar.org/ja/data/model/genba/genba-implfit-Classification-is-completion.png -
fetch_california_housing — scikit-learn 1.6.1 d...
data_home = None , download_if_missing = True , return_X_y = False...False , as_frame = False , n_retries = 3 , delay = 1.0 ) [source]...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html -
d2_absolute_error_score — scikit-learn 1.6.1 do...
sample_weight = None , multioutput = 'uniform_average' )... >>> y_true = [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html -
Product — scikit-learn 1.6.1 documentation
y = make_friedman2 ( n_samples = 500 , noise = 0 , random_state...RBF(length_scale=1) __call__ ( X , Y = None , eval_gradient = False )...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Product.html -
RandomForestRegressor — scikit-learn 1.6.1 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
