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1. Model Quality Summary Metrics

Evidently calculates a few standard model quality metrics: Accuracy, Precision, Recall, F1-score, ROC AUC, and LogLoss.

To support the model performance analysis, Evidently also generates interactive visualizations. They help analyze where the model makes mistakes and come up with improvement ideas.

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2. Class Representation

Shows the number of objects of each class.

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3. Confusion Matrix

Visualizes the classification errors and their type.

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4. Quality Metrics by Class

Shows the model quality metrics for the individual classes. In the case of multi-class problems, it will also include ROC AUC.

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5. Class Separation Quality

A scatter plot of the predicted probabilities shows correct and incorrect predictions for each class.

It serves as a representation of both model accuracy and the quality of its calibration. It also helps visually choose the best probability threshold for each class.

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6. Probability Distribution

A similar view as above, it shows the distribution of predicted probabilities.

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7. ROC Curve

ROC Curve (receiver operating characteristic curve) shows the share of true positives and true negatives at different classification thresholds.

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8. Precision-Recall Curve

The precision-recall curve shows the trade-off between precision and recall for different classification thresholds.

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9. Precision-Recall Table

The table shows possible outcomes for different classification thresholds and prediction coverage. If you have two datasets, the table is generated for both.

Each line in the table defines a case when only top-X% predictions are considered, with a 5% step. It shows the absolute number of predictions (Count) and the probability threshold (Prob) that correspond to this combination.

The table then shows the quality metrics for a given combination. It includes Precision, Recall, the share of True Positives (TP), and False Positives (FP).

This helps explore the quality of the model if you choose to act only on some of the predictions.

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10. Classification Quality by Feature

In this table, we show a number of plots for each feature. To expand the plots, click on the feature name.

In the tab “ALL”, you can see the distribution of classes against the values of the feature. If you compare the two datasets, it visually shows the changes in the feature distribution and in the relationship between the values of the feature and the target.

For each class, you can see the predicted probabilities alongside the values of the feature.

It visualizes the regions where the model makes errors of each type and reveals the low-performance segments. You can compare the distributions and see if the errors are sensitive to the values of a given feature.

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