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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 1.7.0 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 -
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 -
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Demonstration of k-means assumptions — scikit-l...
( X [ y == 0 ][: 500 ], X [ y == 1 ][: 100 ], X [ y == 2 ][: 10...axs = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.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 -
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
