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Chapter 6. AutoML evaluation metrics

AutoML evaluates each trained model with metrics appropriate for the prediction task type. The leaderboard ranks models by the optimized metric. Additional metrics might appear on the leaderboard depending on the task type.

6.1. Optimized metrics by task type
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AutoML selects the optimization metric based on your task type.

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Table 6.1. Optimized metrics
Task typeOptimized metric

Binary classification

Accuracy

Multiclass classification

Accuracy

Regression

R2

Time series forecasting

MASE (Mean Absolute Scaled Error)

Accuracy
The proportion of predictions that are correct. A high accuracy score means the model correctly classifies most inputs.
R2 (R-squared)
The proportion of variance in the target variable that the model explains. A score of 1.0 means the model perfectly predicts the target. A score of 0.0 means the model performs no better than predicting the mean.
MASE (Mean Absolute Scaled Error)
A scale-independent measure of forecast accuracy. AutoML negates MASE values so that higher values indicate better models, consistent with all other metrics on the leaderboard. A raw MASE below 1.0 means the model outperforms a naive baseline forecast.

6.2. Model detail views
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Feature importance
Shows which input features had the most influence on the model’s predictions. Use feature importance to verify that the model relies on meaningful features rather than noise or data artifacts. Feature importance is available for binary classification, multiclass classification, and regression tasks. It is not available for time series forecasting tasks.
Confusion matrix
Shows predicted compared to actual class distributions as a grid. Use the confusion matrix to identify which classes the model confuses most often. The confusion matrix is available for binary and multiclass classification tasks only.
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