How to log your model schema for regression models
Regression Model Overview
Regression models have a continuous, numeric output. (Examples: click-through rates, sales forecasting, customer lifetime value, ETA models, etc.)
Performance Metrics
MAPE, MAE, RMSE, MSE, R-Squared, Mean Error
Allowed Metric Families: Regression
Click here for all valid model types and metric combinations.
Regression Code Example
Example Row
price (float)
pos_approved (bool)
zip_code
age
prediction_score
actual_score
prediction_ts
88.5
False
12345
25
100
90
1671572541
# feature & tag columns can be optionally defined with typing:tag_columns =TypedColumns( inferred=["name"], to_int=["zip_code", "age"])# Declare the schema of the dataframe you're sending (feature columns, predictions, timestamp, actuals) schema =Schema( prediction_id_column_name="prediction_id", timestamp_column_name="prediction_ts", prediction_score_column_name="prediction_score", actual_score_column_name="actual_score", feature_column_names=["price", "pos_approved"], tag_column_names=tag_columns,)# Log the dataframe with the schema mappingresponse = client.log( model_id='sample-model-1', model_version='v1', model_type=ModelTypes.REGRESSION, metrics_validation=[Metrics.REGRESSION], environment=Environments.PRODUCTION, dataframe=test_dataframe, schema=schema)
# features & tags can be optionally defined with typingfeatures ={'score':'88.5','pos_approved':TypedValue(value=False, type=ArizeTypes.INT)}tags ={'zip_code':'12345''age':'25'}response = client.log( model_id='sample-model-1', model_version='v1', model_type=ModelTypes.REGRESSION, environment=Environments.PRODUCTION, features = features, tags=tags prediction_label=100, actual_label=90)
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