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Sklearn Metrics

Calculate model evaluation metrics using scikit-learn

Sklearn Metrics

AI-generated

Summary

Calculate evaluation metrics using scikit-learn

Inputs

  • main (required) — Input data containing the columns required for the selected metric type.

Output shape

main

Single item with the computed metrics as JSON fields.

Examples

Example 1: Classification

Set Metric Type to Classification, specify True Values Column and Predicted Values Column, optionally choose Average Method and Python Path. The node returns accuracy, precision, recall, f1_score, confusion_matrix, support, and unique_classes.

Example 2: Regression

Set Metric Type to Regression, specify True Values Column and Predicted Values Column, and optionally Python Path. The node returns mse, rmse, mae, r2_score, mape, explained_variance, and support.

Example 3: Clustering

Set Metric Type to Clustering, provide Feature Columns (comma‑separated) and Cluster Labels Column, and optionally Python Path. The node returns n_clusters, n_samples, silhouette_score, calinski_harabasz_score, and davies_bouldin_score (or an error if clustering is invalid).

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