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Principal Component Analysis (PCA) on Iris Data...
projection = "3d" , elev =- 150 , azim = 110 ) X_reduced = PCA ( n_components...PCA fig = plt . figure ( 1 , figsize = ( 8 , 6 )) ax = fig . add_subplot...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html -
set_config — scikit-learn 1.7.0 documentation
assume_finite = None , working_memory = None , print_changed_only = None...None , display = None , pairwise_dist_chunk_size = None , enabl...scikit-learn.org/stable/modules/generated/sklearn.set_config.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 -
CategoricalNB — scikit-learn 1.7.0 documentation
alpha = 1.0 , force_alpha = True , fit_prior = True , class_prior...class_prior = None , min_categories = None ) [source] # Naive Bayes...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html -
auto_examples_jupyter.zip
pace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)\n\nplt.show()"...plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35)\n for...scikit-learn.org/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip -
RFE — scikit-learn 1.7.0 documentation
n_features_to_select = None , step = 1 , verbose = 0 , importance_getter = 'auto'...X , y = make_friedman1 ( n_samples = 50 , n_features = 10 , random_state...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html -
Balance model complexity and cross-validated sc...
random_state = 42 , C = 0.01 , max_iter = 1000 )), ] ) param_grid = { ...GridSearchCV(cv=ShuffleSplit(n_splits=30, random_state=0, test_size=None,...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html -
DetCurveDisplay — scikit-learn 1.7.0 documentation
test_size = 0.4 , random_state = 0 ) >>> clf = SVC ( random_state...DetCurveDisplay ( ... fpr = fpr , fnr = fnr , estimator_name = "SVC" ... )...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
LearningCurveDisplay — scikit-learn 1.7.0 docum...
error_score = nan , fit_params = None , ax = None , negate_score = False...>>> X , y = load_iris ( return_X_y = True ) >>> tree = DecisionTreeClassifi...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LearningCurveDisplay.html -
Nearest Centroid Classification — scikit-learn ...
c = y , cmap = cmap_bold , edgecolor = "k" , s = 20 ) plt...shrinkage , np . mean ( y == y_pred )) _ , ax = plt . subplots () DecisionBoundaryDisp...scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html