Arize AI

Classification Models

Classification models predict categories. Arize supports logging the prediction category and the prediction score.
Data Type
Arize Field
Prediction Label
Prediction Score (optional)
Actual Label
Actual Score (optional)

Example ML Use Case

Fraud Models
  • Prediction Label: Is this a Fraud Transaction?
    • Label = "fraud"
  • Prediction Score: How likely is this to be fraud?
    • Score = 0.4
  • Actual Label: "Was this actually a fraud transaction?"
    • Label = "not fraud"
  • Actual Score: "Was this actually fraud?"
    • Score = 0

Performance Metrics Supported

If you include score values, this model type will support both classification and regression metrics.
Field Ingested
Available Metrics
Prediction & Actual Label
Prediction & Actual Score
  • MAPE: Mean Absolute Percent Error
  • MAE: Mean Absolute Error
  • RMSE: Root Mean Square Error
  • MSE: Mean Squared Error
  • R^2: R-Squared or Coefficient of Determination
  • Mean Error: Average Error between Predictions & Actuals
Both Labels and Scores for Predictions and Actuals
All of the above metrics plus:
For some use cases, it may be important to treat a prediction for which no corresponding actual label has been logged yet as having a default negative class actual label.
For example, consider tracking advertisement conversion rates for an ad clickthrough rate model, where the positive class is click and the negative class is no_click. For ad conversion purposes, a prediction without a corresponding actual label for an ad placement is equivalent to logging an explicit no_click actual label for the prediction. In both cases, the result is the same: a user has not converted by clicking on the ad. For AUC-ROC, PR-AUC, and Log Loss performance metrics, Arize supports treating predictions without an explicit actual label as having the negative class actual label by default. In the above example, a click prediction without an actual would be treated as a false positive, because the missing actual for the prediction would by default be assigned to the no_click negative class.
This feature can be enabled for monitors and dashboards via the model performance config section of your model's config page.

Code Example

response = arize.log(
prediction_label="not fraud",
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Classification Models
Example ML Use Case
Performance Metrics Supported
Default Actuals
Code Example