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plot_release_highlights_1_4_0.py
""" ========== Release Highlights for scikit-learn 1.4 ==========...noise = rng.normal(loc=0.0, scale=0.01, size=n_samples) y = 5 *...scikit-learn.org/stable/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py -
GMM covariances — scikit-learn 1.5.0 documentation
bottom = 0.01 , top = 0.95 , hspace = 0.15 , wspace = 0.05 ,...scatterpoints = 1 , loc = "lower right" , prop = dict ( size = 12 ))...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
Classifier comparison — scikit-learn 1.5.0 docu...
C = 0.025 , random_state = 42 ), SVC ( gamma = 2 , C = 1 ,...clf , X , cmap = cm , alpha = 0.8 , ax = ax , eps = 0.5 ) # Plot...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
grid_search_workflow.png
encoding=ISO-8859-1, compression=none keyword=Software, value=www.inkscape.org...0.08468835 width=2031, height=1362, bitDepth=8, colorType=RGBAlpha,...scikit-learn.org/stable/_images/grid_search_workflow.png -
Probability Calibration for 3-class classificat...
y = make_blobs ( n_samples = 2000 , n_features = 2 , centers...centers = 3 , random_state = 42 , cluster_std = 5.0 ) X_train ,...scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html -
Support Vector Regression (SVR) using linear an...
# svr_rbf = SVR ( kernel = "rbf" , C = 100 , gamma = 0.1 , epsilon...epsilon = 0.1 ) svr_lin = SVR ( kernel = "linear" , C = 100 , gamma...scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html -
BallTree — scikit-learn 1.5.0 documentation
return_distance == False (d,i) if return_distance == True d ndarray...count if count_only == True ind if count_only == False and return_distance...scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html -
Gradient Boosting Out-of-Bag estimates — scikit...
n_splits = None ): cv = KFold ( n_splits = n_splits ) cv_clf = ensemble...) x1 = random_state . uniform ( size = n_samples ) x2 = random_state...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html -
Understanding the decision tree structure — sci...
iris = load_iris () X = iris . data y = iris . target...y_test = train_test_split ( X , y , random_state = 0 ) clf = DecisionTreeClassifi...scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html -
plot_adaboost_regression.py
""" ========== Decision Tree Regression with AdaBoost ==========...y_1, color=colors[1], label="n_estimators=1", linewidth=2) plt.plot(X,...scikit-learn.org/stable/_downloads/2da78c80da33b4e0d313b0a90b923ec8/plot_adaboost_regression.py
