Arize AI
Search…
⌃K

Binary Classification With Just Labels

A category is predicted, and no probability is associated with the label

Binary Classification With Labels Overview

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

Performance Metrics

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

Binary Classification With Labels 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"
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",
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",
actual_label="fraud"
)
This example is for the python batch ingestion method, for other languages, please refer to our examples.
Example predictions: is fraud, will buy/click, will churn