Multi Class Classification

Multi-Class Classification Overview

A classification model with more than two classes.

Supported Metrics

Micro-Averaged Precision, Micro-Averaged Recall, Macro-Averaged Precision, Macro-Averaged Recall, 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	A prediction that has 1 label i.e. A passenger can only be in EITHER economy, business, OR first-class	- prediction scores (dictionary) - actual scores (dictionary, optional)
Multi-Label	A prediction that has multiple labels i.e. A song can be multiple genres such as ‘pop-rock’	- prediction scores (dictionary) - threshold scores (dictionary) - actual scores (dictionary, optional)

Single-Label Use Case

Python Pandas Batch
Python Single Record

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Google Colab

Single-Label Use Case Colab

Example Row

prediction_scores	actual_scores
`[{"class_name": "economy_class", "score": 0.81},{"class_name": "business_class", "score": 0.42},{"class_name": "first_class", "score": 0.35}]`	`[{"class_name": "economy_class", "score": 1}]`

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,
)

Google Colab

Single-Label Use Case Single RecordColab

# Predicting likelihood of Economy, Business, or First Class
"""
example_record = {
"prediction_scores":{
    "economy_class":0.81,
    "business_class":0.42,
    "first_class":0.35
},
"actual_scores": {
    "first_class": 1
}
}
"""
prediction_label = MultiClassPredictionLabel(
    prediction_scores=record["prediction_scores"],
)
actual_label = MultiClassActualLabel(
    actual_scores=record["actual_scores"],
)
response = arize_client.log(
    model_id="multiclass-classification-single-label-example",
    model_version= "v1",
    model_type=ModelTypes.MULTI_CLASS,
    prediction_id=record["prediction_id"],
    prediction_label=prediction_label,
    actual_label=actual_label,
    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.

class MultiClassPredictionLabel(
    prediction_scores: Dict[str, Union[float, int]]
)

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 delayed actuals.

class MultiClassActualLabel(
    actual_scores: Dict[str, Union[float, int]]
)

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

Single Record Logging

Multi-Label Use Case

Python Pandas Batch
Python Single Record

Google Colab

Multi-Label Use Case Colab

Example Row

prediction_scores	threshold_scores	actual_scores
`[{"class_name": "jazz", "score": 0.81},{"class_name": "rock", "score": 0.42},{"class_name": "pop", "score": 0.35}]`	`[{"class_name": "jazz", "score": 0.5},{"class_name": "rock", "score": 0.4},{"class_name": "pop", "score": 0.6}]`	`[{"class_name": "rock", "score": 1}]`

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

Code Example

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",
)

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

Google Colab

Multi-Label Use Case, Single Record Colab

#predicting song genre within pop, rock, and jazz categories
"""
example_record = {
"prediction_id": "57437c5d-0cd2-48bf-aa91-379302fd0fe4",
"prediction_scores": {
"pop": 0.5044088627000001,
"rock": 0.3688503145,
"jazz": 0.1267408228
},
"threshold_scores": {
"pop": 0.50,
"rock": 0.40,
"jazz": 0.10
},
"actual_scores": {
"pop": 1,
"jazz": 1
}
"""

prediction_label = MultiClassPredictionLabel(
    prediction_scores=record["prediction_scores"],
        threshold_scores=record["threshold_scores"],  #required for multi-label
)
actual_label = MultiClassActualLabel(
    actual_scores=record["actual_class"],
)
response = arize_client.log(
    model_id="multiclass-classification-multi-label-example",
    model_version= "v1",
    model_type=ModelTypes.MULTI_CLASS,
    prediction_id=record["prediction_id"],
    prediction_label=prediction_label,
    actual_label=actual_label,
    environment=Environments.PRODUCTION
)

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.

class MultiClassPredictionLabel(
    prediction_scores: Dict[str, Union[float, int]]
    threshold_scores: Dict[str, Union[float, int]] = None #for multi-label use cases
)

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 delayed actuals.

class MultiClassActualLabel(
    actual_scores: Dict[str, Union[float, int]]
)

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

Single Record Logging

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
Single-Label	For each prediction, the class with the highest prediction score is the prediction label	The class with an actual score of 1 is the actual label
Multi-Label	For each class, there must exist a prediction score and threshold score. If the prediction score > threshold score, the class is a part of the prediction label	Each class with an actual score of 1 is part of the actual label

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Multi Class Classification