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Detection error tradeoff (DET) curve — scikit-l...
In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two ...scikit-learn.org/stable/auto_examples/model_selection/plot_det.html -
Evaluate the performance of a classifier with C...
Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is e...scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html -
Effect of varying threshold for self-training —...
This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrai...scikit-learn.org/stable/auto_examples/semi_supervised/plot_self_training_varying_threshold.html -
Set up your development environment — scikit-le...
Fork the scikit-learn repository: First, you need to create an account on GitHub (if you do not already have one) and fork the project repository by clicking on the ‘Fork’ button near the top of th...scikit-learn.org/stable/developers/development_setup.html -
Robust vs Empirical covariance estimate — sciki...
The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to use a robust estimator of covariance to guarant...scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html -
Gaussian Mixture Model Sine Curve — scikit-lear...
This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset is formed by 100 points loosely spaced fo...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html -
Lagged features for time series forecasting — s...
This example demonstrates how Polars-engineered lagged features can be used for time series forecasting with HistGradientBoostingRegressor on the Bike Sharing Demand dataset. See the example on Tim...scikit-learn.org/stable/auto_examples/applications/plot_time_series_lagged_features.html -
Prediction Intervals for Gradient Boosting Regr...
This example shows how quantile regression can be used to create prediction intervals. See Features in Histogram Gradient Boosting Trees for an example showcasing some other features of HistGradien...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html -
Multi-class AdaBoosted Decision Trees — scikit-...
This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al 1. The core prin...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html -
Imputing missing values with variants of Iterat...
The IterativeImputer class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn. In this example we compare some...scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html