Performance Tracking

Overview

The model performance tells us how well a model performs on its task. A poorly performing model can have significant business implications.

What is being tracked?

Performance metrics

Model Task Type	Metric	Description
Binary Classification	Accuracy	(TP + TN) / (TP + TN + FP + FN)
Binary Classification	True Positive Rate/Recall	TP / (TP + FN)
Binary Classification	False Positive Rate	FP / (FP + TN)
Binary Classification	Precision	TP / (TP + FP)
Binary Classification	F1 Score	2 * ( Precision * Recall ) / ( Precision + Recall )
Binary Classification	AUROC	Area Under the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate against the false positive rate
Binary Classification	Binary Cross Entropy	Measures the difference between the predicted probability distribution and the true distribution
Binary Classification	Geometric Mean	Square Root of ( Precision * Recall )
Binary Classification	Calibrated Threshold	A threshold that balances precision and recall at a particular operating point
Binary Classification	Data Count	The number of events where target and output are both not NULL. This will be used as the denominator when calculating accuracy.
Binary Classification	Expected Calibration Error	Measures the difference between predicted probabilities and empirical probabilities
Multi Classification	Accuracy	(Number of correctly classified samples) / ( Data Count ). Data Count refers to the number of events where the target and output are both not NULL
Multi Classification	Log Loss	Measures the difference between the predicted probability distribution and the true distribution, in a logarithmic scale
Regression	Coefficient of determination (R-squared)	Measures the proportion of variance in the dependent variable that is explained by the independent variables
Regression	Mean Squared Error (MSE)	Average of the squared differences between the predicted and true values
Regression	Mean Absolute Error (MAE)	Average of the absolute differences between the predicted and true values
Regression	Mean Absolute Percentage Error (MAPE)	Average of the absolute percentage differences between the predicted and true values
Regression	Weighted Mean Absolute Percentage Error (WMAPE)	The weighted average of the absolute percentage differences between the predicted and true values
Ranking	Mean Average Precision (MAP)—for binary relevance ranking only	Measures the average precision of the relevant items in the top-k results
Ranking	Normalized Discounted Cumulative Gain (NDCG)	Measures the quality of the ranking of the retrieved items, by discounting the relevance scores of items at lower ranks

Why is it being tracked?

• Model performance tells us how well a model is doing on its task. A poorly performing model can have significant business implications.

• The volume of decisions made on the basis of the predictions give visibility into the business impact of the model.

What steps should I take based on this information?

• For changes in model performance—again, the best way to cross-verify the results is by checking the Data Drift Tab ). Once you confirm that the performance issue is not due to the data, you need to assess if the change in performance is due to temporary factors, or due to longer-lasting issues.

• You can check if there are any lightweight changes you can make to help recover performance—for example, you could try modifying the decision threshold.

• Retraining the model with the latest data and redeploying it is usually the solution that yields the best results, although it may be time-consuming and expensive.

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