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make_low_rank_matrix — scikit-learn 1.6.1 docum...
faces TF-IDF vectors of text documents crawled from the web Read...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_low_rank_matrix.html -
Statistical comparison of models using grid sea...
Documentation for GridSearchCV i Fitted...SVC(random_state=0) SVC ? Documentation for SVC SVC(random_state=0)...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_stats.html -
Target Encoder’s Internal Cross fitting — sciki...
The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship ...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Multiclass Receiver Operating Characteristic (R...
This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers. ROC curves typically feature true positive rate (TPR) on the ...scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html -
Illustration of prior and posterior Gaussian pr...
This example illustrates the prior and posterior of a GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions. ...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html -
Gradient Boosting Out-of-Bag estimates — scikit...
Out-of-bag (OOB) estimates can be a useful heuristic to estimate the “optimal” number of boosting iterations. OOB estimates are almost identical to cross-validation estimates but they can be comput...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html -
Balance model complexity and cross-validated sc...
This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html -
Joint feature selection with multi-task Lasso —...
The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a t...scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html -
1.4. Support Vector Machines — scikit-learn 1.6...
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high ...scikit-learn.org/stable/modules/svm.html -
mean_absolute_percentage_error — scikit-learn 1...
Gallery examples: Lagged features for time series forecastingscikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html