Multi-Class Classification

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Last updated 9 months ago

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Multi-Class Classification Overview

A classification model with more than two classes.

Supported Metrics

, , , , Precision for a Class, Recall for a Class

How To Log Multi-Class Data

Log multi-class classification models based on your use case

Use Case

Description

Expected Fields

Single-Label Use Case

Example Row

prediction_scores

actual_scores

Note: class economy_class has the highest prediction score and will be the prediction label

Code Example

Note: class economy_class has the highest prediction score and will be the prediction label

Multi-Class Prediction

Log multi-class prediction values using the MultiClassPredictionLabel object, which can be assigned to the prediction_label parameter.

Prediction scores are required to use MULTI_CLASS models in Arize.

Multi-Class Actual Values

Log multi-class actual values by using the MultiClassActualLabel object, which can be assigned to the actual_label parameter.

For more information on Python Single Record Logging API Reference, visit here:

Multi-Label Use Case

Example Row

Note: classes jazz and rock have prediction scores > threshold scores and will be part of the prediction label.

Code Example

Multi-Class Prediction & Threshold Values

Log multi-class prediction and threshold values using the MultiClassPredictionLabel object, which can be assigned to the prediction_label parameter.

Prediction scores are required to use MULTI_CLASS models in Arize.

Multi-Class Actual Values

Log multi-class actual values by using the MultiClassActualLabel object, which can be assigned to the actual_label parameter.

For more information on Python Single Record Logging API Reference, visit here:

Inferring Labels From Uploaded Scores

To calculate metrics and visualize & troubleshoot data for multi-class models, Arize automatically infers prediction & actual labels from the scores that you upload.

Learn how each case is determined below.

Last updated 9 months ago

Multi-Class Classification Overview

A classification model with more than two classes.

Supported Metrics

, , , , Precision for a Class, Recall for a Class

How To Log Multi-Class Data

Log multi-class classification models based on your use case

Use Case

Description

Expected Fields

Single-Label Use Case

Example Row

prediction_scores

actual_scores

Note: class economy_class has the highest prediction score and will be the prediction label

Code Example

schema = Schema(
    prediction_id_column_name="prediction_id",
    prediction_score_column_name="prediction_scores",
    actual_score_column_name="actual_scores",
)

response = arize_client.log(
    model_id='multiclass-classification-single-label-example', 
    model_version= "v1",
    model_type=ModelTypes.MULTI_CLASS,
    dataframe=example_dataframe,
    schema=schema,
    environment=Environments.PRODUCTION,
)

Note: class economy_class has the highest prediction score and will be the prediction label

Multi-Class Prediction

Log multi-class prediction values using the MultiClassPredictionLabel object, which can be assigned to the prediction_label parameter.

Prediction scores are required to use MULTI_CLASS models in Arize.

Multi-Class Actual Values

Log multi-class actual values by using the MultiClassActualLabel object, which can be assigned to the actual_label parameter.

Actual values are optional, and can be sent later via .

For more information on Python Single Record Logging API Reference, visit here:

Multi-Label Use Case

Example Row

prediction_scores

threshold_scores

actual_scores

Note: classes jazz and rock have prediction scores > threshold scores and will be part of the prediction label.

Code Example

Multi-Class Prediction & Threshold Values

Log multi-class prediction and threshold values using the MultiClassPredictionLabel object, which can be assigned to the prediction_label parameter.

Prediction scores are required to use MULTI_CLASS models in Arize.

Multi-Class Actual Values

Log multi-class actual values by using the MultiClassActualLabel object, which can be assigned to the actual_label parameter.

Actual values are optional, and can be sent later via .

For more information on Python Single Record Logging API Reference, visit here:

Inferring Labels From Uploaded Scores

To calculate metrics and visualize & troubleshoot data for multi-class models, Arize automatically infers prediction & actual labels from the scores that you upload.

Learn how each case is determined below.

Use Case

Prediction Label Determination

Actual Label Determination

schema = Schema(
    prediction_id_column_name="prediction_id",
    prediction_score_column_name="prediction_scores",
    multi_class_threshold_scores_column_name="threshold_scores"
    actual_score_column_name="actual_scores",
)