- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 231 - 240 of 1,992 for = (0.59 sec)
-
SkewedChi2Sampler — scikit-learn 1.6.1 document...
skewedness = 1.0 , n_components = 100 , random_state = None ) [source]...n_components = 10 , ... random_state = 0 ) >>> X_features = chi2_feature...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html -
jaccard_score — scikit-learn 1.6.1 documentation
labels = None , pos_label = 1 , average = 'binary' , sample_weight...sample_weight = None , zero_division = 'warn' ) [source] # Jaccard...scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html -
Label Propagation digits: Demonstrating perform...
n_total_samples = len ( y ) n_labeled_points = 40 indices = np . arange...lp_model = LabelSpreading ( gamma = 0.25 , max_iter = 20 ) lp_model...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.html -
Column Transformer with Mixed Types — scikit-le...
y = fetch_openml ( "titanic" , version = 1 , as_frame = True...attribute: # X = titanic.frame.drop('survived', axis=1) # y = titanic.frame['survived']...scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html -
Gradient Boosting regularization — scikit-learn...
test_size = 0.8 , random_state = 0 ) original_params = { "n_estimators"...color = color , label = label , ) plt . legend ( loc = "upper...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html -
5. Visualizations — scikit-learn 1.6.1 document...
y = load_wine ( return_X_y = True ) y = y == 2 # make...y_test = train_test_split ( X , y , random_state = 42 ) svc = SVC...scikit-learn.org/stable/visualizations.html -
A demo of the Spectral Co-Clustering algorithm ...
n_clusters = 5 , noise = 5 , shuffle = False , random_state = 0 ) plt..., rows , columns = make_biclusters ( shape = ( 300 , 300 ), n_clusters...scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html -
Probability calibration of classifiers — scikit...
): this_X = X_train [ y_train == this_y ] this_sw = sw_train [...n_samples = n_samples , centers = centers , shuffle = False , random_state...scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html -
Comparing random forests and the multi-output m...
edgecolor = "k" , c = "navy" , s = s , marker = "s" , alpha = a , label...edgecolor = "k" , c = "c" , s = s , marker = "^" , alpha = a , label...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html -
DetCurveDisplay — scikit-learn 1.6.1 documentation
sample_weight = None , pos_label = None , name = None , ax = None ,..., test_size = 0.4 , random_state = 0 ) >>> clf = SVC ( random_state...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html