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Binary Classification With Prediction Score

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

Binary Classification With Prediction Score Overview

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

Performance Metrics

AUC, PR-AUC, Log Loss

Binary Classification w/ Prediction Score 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
str | bool
"fraud"
0 or 1
0

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_label_column_name="ACTUAL",
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_label="fraud",
actual_score=0
)
This example is for the python batch ingestion method, for other languages, please refer to our examples.
Example predictions: Fraud: 0.0876, or Click: 0.23.