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plot_pca_iris.py
of the Iris dataset # ---------- # # Let's first plot the pairs...title="Classes", ) ax.add_artist(legend1) plt.show() # %% # PCA...scikit-learn.org/stable/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py -
GradientBoostingClassifier — scikit-learn 1.7.1...
None , tol = 0.0001 , ccp_alpha = 0.0 ) [source] # Gradient Boosting...increase in bias. Values must be in the range (0.0, 1.0] . criterion...scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html -
HistGradientBoostingRegressor — scikit-learn 1....
{‘squared_error’, ‘absolute_error’, ‘gamma’, ‘poisson’, ‘quantile’}, def...'squared_error' , * , quantile = None , learning_rate = 0.1 , max_iter =...scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html -
HalvingRandomSearchCV — scikit-learn 1.7.1 docu...
object (e.g., function) with signature scorer(estimator, X, y) ....param_distributions , * , n_candidates = 'exhaust' , factor = 3 , resource...scikit-learn.org/stable/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html -
LassoLarsIC — scikit-learn 1.7.1 documentation
2.2222 , - 1.1111 , 0 , - 1.1111 , - 2.2222 ] >>> reg . fit (...], [ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]] >>> y = [ - 2.2222...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html -
HDBSCAN — scikit-learn 1.7.1 documentation
“brute”, “kd_tree”, “ball_tree”}, default=”auto” Exactly which algorithm...None , alpha = 1.0 , algorithm = 'auto' , leaf_size = 40 , n_jobs...scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html -
SparsePCA — scikit-learn 1.7.1 documentation
ridge_alpha = 0.01 , max_iter = 1000 , tol = 1e-08 , method = 'lars' , n_jobs...stopping condition. method {‘lars’, ‘cd’}, default=’lars’ Method to be...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparsePCA.html -
plot_classifier_comparison.zip
= X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:,...X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 # just plot the dataset...scikit-learn.org/stable/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip -
OneClassSVM — scikit-learn 1.7.1 documentation
{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’...'scale' , coef0 = 0.0 , tol = 0.001 , nu = 0.5 , shrinking = True...scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html -
Binarizer — scikit-learn 1.7.1 documentation
], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , - 1. ]] >>> transformer...1.], [1., 0., 0.], [0., 1., 0.]]) fit ( X , y = None ) [source] #...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html