Arize AI
Search…
Classification

Classification Models

Classification models predict categories. Arize supports logging the prediction category and the prediction score.
Field
Data Type
Arize Field
Prediction Label
string
prediction_label
Prediction Score (optional)
float
prediction_score
Actual Label
string
actual_label
Actual Score (optional)
float
actual_score

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(
model_id='sample-model-1',
model_version='v1",
model_type=ModelTypes.SCORE_CATEGORICAL,
...
prediction_label="not fraud",
prediction_score=0.3,
actual_label="fraud",
actual_score=0.6
)
Copy link
Outline
Classification Models
Example ML Use Case
Performance Metrics Supported
Default Actuals
Code Example