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Binary Classification With Score and All Labels

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

Binary Classification With Scores and Labels Overview

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

Performance Metric

AUC, PR-AUC, Log Loss, MAPE, MAE, RMSE, MSE, R2, Mean Error

Examples

File Type
Link
Python Batch

Binary Classification With Scores and Labels Model 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
str | bool
"not fraud"
float
0.3
str | bool
"fraud"

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_label_column_name="PREDICTION",
prediction_score_column_name="PREDICTION_SCORE",
actual_label_column_name="ACTUAL",
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_label="not fraud",
prediction_score=0.3,
actual_label="fraud"
)
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.