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Binary Classification Label and All Scores

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

Scored Classification Variant Overview

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

Performance Metric

AUC, Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity

Scored Classification Variant 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
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_label_column_name="PREDICTION",
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_label="not fraud",
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.