Performance Tracking
Platform Guide
What is being tracked?

Decisions  The postprediction business decisions made as a result of the model output. Decisions are calculated before client.publish_event() (they're not inferred by Fiddler). For binary classification models, a decision is usually determined using a threshold. For multiclass classification models, it's usually determined using the argmax value of the model outputs.

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 (Rsquared)  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 topk 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 decisions, if there is an increase or decrease in approvals, we can crosscheck with the average prediction and prediction drift trendlines on the Data Drift Tab. In general, the average prediction value should increase with an increase in the number of approvals, and viceversa.
 For changes in model performance—again, the best way to crossverify 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 longerlasting 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 timeconsuming and expensive.
Reference
 See our article on The Rise of MLOps Monitoring
[^1]: Join our community Slack to ask any questions
Updated 5 days ago