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Decision Boundaries of Multinomial and One-vs-R...
n_samples = 1_000 , centers = centers , random_state = 40 ) transformation..., ax = plt . subplots ( figsize = ( 6 , 4 )) scatter = ax . scatter...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html -
Poisson regression and non-normal loss — scikit...
axes = plt . subplots ( nrows = 2 , ncols = 4 , figsize = ( 16..., ax = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html -
Multilabel classification — scikit-learn 1.7.0 ...
if transform == "pca" : X = PCA ( n_components = 2 ) . fit_transform...elif transform == "cca" : X = CCA ( n_components = 2 ) . fit ( X...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multilabel.html -
Plot multi-class SGD on the iris dataset — scik...
mean ( axis = 0 ) std = X . std ( axis = 0 ) X = ( X - mean ) /...dataset X = iris . data [:, : 2 ] y = iris . target colors = "bry"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html -
plot_multi_metric_evaluation.rst.txt
py: ========== Demonstration of multi-metric...cross_val_score and GridSearchCV ========== Multiple metric parameter...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
Plot class probabilities calculated by the Voti...
random_state = 123 ) clf2 = RandomForestClassifi ( n_estimators = 100..., random_state = 123 ) clf3 = GaussianNB () X = np . array ([[...scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html -
Effect of model regularization on training and ...
n_informative = 50 , shuffle = False , noise = 1.0 , coef = True , random_state...color = "k" , linewidth = 2 , linestyle = "--" , label = f "Optimum...scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html -
adjusted_mutual_info_score — scikit-learn 1.7.0...
average_method = 'arithmetic' ) [source] # Adjusted...AMI is given as: AMI ( U , V ) = [ MI ( U , V ) - E ( MI ( U ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html -
Hashing feature transformation using Totally Ra...
y = make_circles ( factor = 0.5 , random_state = 0 , noise...random_state = 0 , max_depth = 3 ) X_transformed = hasher . fit_transform...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html -
non_negative_factorization — scikit-learn 1.7.0...
W = None , H = None , n_components = 'auto' , * , init = None...tol = 0.0001 , max_iter = 200 , alpha_W = 0.0 , alpha_H = 'same'...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html