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Binary Classification Just Scores

A category is predicted and has a probability associated with the label

Binary Classification Just Scores Overview

This model variant only includes the model's prediction score and actual score in the model schema.

Performance Metric

MAPE, MAE, RMSE, MSE, R2, Mean Error

Binary Classification Just Scores Schema Parameters

Arize Field
Data Type
Example
str
"prediction_id"
timestamp
str
"prediction_ts"
List[str]
["MERCHANT_TYPE", "ENTRY_MODE", "STATE"]
List[str]
["ZIP_CODE", "GENDER", "AGE"]
model_id
str
'sample-model-1'
str
'v1'
model_type
str
ModelTypes.SCORE_CATEGORICAL
str
Environments.PRODUCTION
float
0.3
float
0.6

Code Example

# 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=["MERCHANT_TYPE", "ENTRY_MODE", "STATE"],
tag_column_names=["ZIP_CODE", "GENDER", "AGE"]
)
# Log the dataframe with the schema mapping
response = arize_client.log(
model_id='sample-model-1',
model_version='v1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
dataframe=test_dataframe,
schema=schema,
prediction_score=0.3,
actual_score=0.6
)
This example is for the python batch ingestion method, for other languages, please refer to our examples.