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Comparing anomaly detection algorithms for outl...
make_blobs ( centers = [[ 2 , 2 ], [ - 2 , - 2 ]], cluster_std = [...make_blobs ( centers = [[ 2 , 2 ], [ - 2 , - 2 ]], cluster_std = [...scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html -
Overview of multiclass training meta-estimators...
code_size = 2 ) cv_results_tree = cross_validate..., X , y , cv = cv , n_jobs = 2 ) cv_results_ovo = cross_validate...scikit-learn.org/stable/auto_examples/multiclass/plot_multiclass_overview.html -
Model selection with Probabilistic PCA and Fact...
rand ( n_features ) + sigma / 2.0 X_hetero = X + rng . randn (...shrinkages = np . logspace ( - 2 , 0 , 30 ) cv = GridSearchCV (...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
Plot individual and voting regression predictio...
'friedman_mse' min_samples_split 2 min_samples_leaf 1 min_weight_fraction_leaf...max_depth None min_samples_split 2 min_samples_leaf 1 min_weight_fraction_leaf...scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html -
Novelty detection with Local Outlier Factor (LO...
2 ) X_train = np . r_ [ X + 2 , X - 2 ] # Generate...randn ( 20 , 2 ) X_test = np . r_ [ X + 2 , X - 2 ] # Generate...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
root_mean_squared_log_error — scikit-learn 1.7....
2.5 , 7 ] >>> y_pred = [ 2.5 , 5 , 4 , 8 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_log_error.html -
7.6. Random Projection — scikit-learn 1.7.2 doc...
2. Gaussian random projection #...scikit-learn.org/stable/modules/random_projection.html -
HuberRegressor vs Ridge on dataset with strong ...
2.0 , size = 4 ) X_outliers [: 2 , :] += X . max...y_outliers [: 2 ] += y . min () - y . mean () / 4.0 y_outliers [ 2 :] +=...scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html -
safe_sparse_dot — scikit-learn 1.7.2 documentation
2 ], [ 3 , 4 ], [ 5 , 6 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.safe_sparse_dot.html -
Visualizing cross-validation behavior in scikit...
ylim = [ n_splits + 2.2 , - 0.2 ], xlim = [ 0 , 100 ], )...cmap = cmap_cv , vmin =- 0.2 , vmax = 1.2 , ) # Plot the data classes...scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html