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TSNE — scikit-learn 1.6.1 documentation
init = 'pca' , verbose = 0 , random_state = None , method = 'barnes_hut'...n_components = 2 , * , perplexity = 30.0 , early_exaggeration = 12.0...scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html -
ROC Curve with Visualization API — scikit-learn...
y = load_wine ( return_X_y = True ) y = y == 2 X_train...y_test = train_test_split ( X , y , random_state = 42 ) svc = SVC...scikit-learn.org/stable/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html -
favicon_64x64_16bit.png
35273367 width=64, height=64, bitDepth=8, colorType=RGBAlpha, c...whitePointX=31270, whitePointY=32900, redX=64000, redY=33000, greenX=30000,...www.elastic.co/favicon_64x64_16bit.png -
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 1.6.1 ...
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 -
ridge_regression — scikit-learn 1.6.1 documenta...
sample_weight = None , solver = 'auto' , max_iter = None , tol = 0.0001..., verbose = 0 , positive = False , random_state = None , return_n_iter...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ridge_regression.html -
RandomForestClassifier — scikit-learn 1.6.1 doc...
n_estimators = 100 , * , criterion = 'gini' , max_depth = None , min_samples_split...min_weight_fraction_leaf = 0.0 , max_features = 'sqrt' , max_leaf_nodes = None ,...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
