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Arize enum to specify your model type represented in the platform and validate applicable performance metrics.

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Model Types

from arize.utils.types import ModelTypes
Specify a model_type when logging a prediction for the first time for a new model.

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Method

list_types() View Source Returns a list of all model types. 
ModelTypes.list_types()
Use CaseSDK ModelTypeDescription
RegressionModelTypes.REGRESSIONRegression models predict continuous values
Binary ClassificationModelType.BINARY_CLASSIFICATIONBinary classification models predict only two categorical values, typically represented as 0 or 1
Multi ClassModelType.MULTI_CLASSMulticlass models predict multiple categorical values
RankingModelType.RANKINGRanking models predict the relative ordering of a set of items based on their features
Natural Language Processing (NLP)ModelType.SCORE_CATEGORICALNLP models are categorical models specifically designed to work with text data and perform various tasks (i.e. sentiment analysis and language translation)
Computer Vision (CV)ModelType.SCORE_CATEGORICALCV models are categorical models specifically designed to work with visual data and perform various tasks (i.e. object detection and image classification)
Object DetectionModelTypes.OBJECT_DETECTIONObject detection models identify and locate objects within images or videos by assigning them specific bounding boxes

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Code Example

response = arize_client.log(
    model_id='sample-binary-classification-model', 
    ...
    model_type=ModelTypes.BINARY_CLASSIFICATION
)

response = arize_client.log(
    model_id='sample-regression-model', 
    ...
    model_type=ModelTypes.REGRESSION
)

response = arize_client.log(
    model_id='sample-ranking-model', 
    ...
    model_type=ModelTypes.RANKING
)