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VotingRegressor — scikit-learn 1.7.2 documentation
weights = None , n_jobs = None , verbose = False ) [source]...n_estimators = 10 , random_state = 1 ) >>> r3 = KNeighborsRegressor...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingRegressor.html -
DotProduct — scikit-learn 1.7.2 documentation
y = make_friedman2 ( n_samples = 500 , noise = 0 , random_state...DotProduct ( sigma_0 = 1.0 , sigma_0_bounds = (1e-05, 100000.0)...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html -
1.17. Neural network models (supervised) — scik...
>>> clf = MLPClassifier ( solver = 'lbfgs' , alpha = 1e-5 , ......MLPClassifier(alpha=1e-05, hidden_layer_sizes=(5, 2), random_state=1, solver='lbfgs')...scikit-learn.org/stable/modules/neural_networks_supervised.html -
WhiteKernel — scikit-learn 1.7.2 documentation
x_2) = noise\_level \text{ if } x_i == x_j \text{ else...>>> X , y = make_friedman2 ( n_samples = 500 , noise = 0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html -
fetch_lfw_pairs — scikit-learn 1.7.2 documentation
subset = 'train' , data_home = None , funneled = True , resize...resize = 0.5 , color = False , slice_ = (slice(70, 195, None),...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_pairs.html -
OneToOneFeatureMixin — scikit-learn 1.7.2 docum...
y = None ): ... self . n_features_in_ = X . shape [...dtype=object) get_feature_names_out ( input_features = None )...scikit-learn.org/stable/modules/generated/sklearn.base.OneToOneFeatureMixin.html -
FeatureHasher — scikit-learn 1.7.2 documentation
n_features=1048576 , * , input_type='dict' , dtype=<class 'numpy.float64'>...FeatureHasher >>> h = FeatureHasher ( n_features = 10 ) >>> D = [{ 'dog'...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html -
KNNImputer — scikit-learn 1.7.2 documentation
missing_values = nan , n_neighbors = 5 , weights = 'uniform' , metric...metric = 'nan_euclidean' , copy = True , add_indicator = False...scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html -
make_s_curve — scikit-learn 1.7.2 documentation
n_samples = 100 , * , noise = 0.0 , random_state = None ) [source]...>>> X , t = make_s_curve ( noise = 0.05 , random_state = 0 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html -
3.3. Tuning the decision threshold for class pr...
weights = [ 0.1 , 0.9 ], random_state = 0 ) >>> pos_label = 0 >>>... >>> X , y = make_classification ( random_state = 0 ) >>> classifier...scikit-learn.org/stable/modules/classification_threshold.html