Metrics

This module contains functions related to metrics.

Regression metrics

This module contains functions related to Regression metrics.

plot residuals distribution

plot_residuals_distribution

Trained model of LinearRegression or RandomForestRegressor. For an example of performing image:

Examples

• plot_residuals_distribution with examples: Example usage of sklearn.linear_model.LinearRegression using the diabetes dataset (regression).

References

• “Normal Probability Plot of Residuals”.

Classification metrics

This module contains functions related to Classification metrics.

plot calibration

plot_calibration

Trained model of LogisticRegression or RandomForestClassifier. For an example of performing image:

Examples

• plot_calibration with examples: Example usage of sklearn.linear_model.LogisticRegression using the iris dataset

References

• “scikit-learn PCA”.

plot precision recall

plot_precision_recall

Trained model of LogisticRegression or RandomForestClassifier. For an example of performing image:

Examples

• plot_precision_recall with examples: Example usage of LogisticRegression using the iris dataset

References

• “scikit-learn precision-recall”.

plot roc

plot_roc

Trained model of LogisticRegression or RandomForestClassifier. For an example of performing image:

Examples

• plot_roc_curve with examples: Example usage of LogisticRegression using the iris dataset

References

• “scikit-learn roc”.

Clustering metrics

This module contains functions related to Clustering metrics.

plot silhouette

plot_silhouette

Trained model of KMeans or MiniBatchKMeans. For an example of performing image:

Examples

• plot_silhouette with examples: Example usage of KMeans using the iris dataset

References

• “scikit-learn k-means”.

• “scikit-learn mini-batch-k-means”.