utils.types.Metrics

​
​View Source on Github​
Arize enum for metrics grouping when validating a schema column in log() call.
Use Case
SDK Metric
Metric
Regression
Metrics.REGRESSION
MAPE, MAE, RMSE, MSE, R-Squared, Mean Error
Classification
Metrics.CLASSIFICATION
Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity
Ranking
Metrics.RANKING
NDCG
AUC & LogLoss
Metrics.AUC_LOG_LOSS
AUC, PR-AUC, Log Loss
Ranking Label
Metrics.RANKING_LABEL
GroupAUC, MAP, MRR
Method
repr()
To view applicable metrics, pass in your desired SDK Metric from above.
repr(Metrics.[SDK Metric])
Code Example
response = arize_client.log(
    model_id='sample-binary-classification-model', 
    ...
    metrics_validation=[Metrics.CLASSIFICATION]
​
)
​
response = arize_client.log(
    model_id='sample-regression-model', 
    ...
    metrics_validation=[Metrics.REGRESSION]
)
​
response = arize_client.log(
    model_id='sample-ranking-model', 
    ...
    metrics_validation=[Metrics.RANKING, Metrics.RANKING_LABEL]
)

Use Case	SDK Metric	Metric
Regression	`Metrics.REGRESSION`	MAPE, MAE, RMSE, MSE, R-Squared, Mean Error
Classification	`Metrics.CLASSIFICATION`	Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity
Ranking	`Metrics.RANKING`	NDCG
AUC & LogLoss	`Metrics.AUC_LOG_LOSS`	AUC, PR-AUC, Log Loss
Ranking Label	`Metrics.RANKING_LABEL`	GroupAUC, MAP, MRR

utils.types.ModelTypes

utils.types.Environments

Last modified 6mo ago