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utils.types.ModelTypes

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​View source on Github​
Arize enum to specify your model type represented in the platform and validate applicable performance metrics.

Model Types

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

Method

list_types()
View Source
Returns a list of all model types.
ModelTypes.list_types()
Use Case
SDK ModelType
Description
​Regression​
ModelTypes.REGRESSION
Regression models predict continuous values
ModelType.BINARY_CLASSIFICATION
Binary classification models predict only two categorical values, typically represented as 0 or 1
​Multi Class​
ModelType.SCORE_CATEGORICAL
Multiclass models predict multiple categorical values
​Ranking ​
ModelType.RANKING
Ranking models predict the relative ordering of a set of items based on their features
ModelType.SCORE_CATEGORICAL
NLP models are categorical models specifically designed to work with text data and perform various tasks (i.e. sentiment analysis and language translation)
ModelType.SCORE_CATEGORICAL
CV models are categorical models specifically designed to work with visual data and perform various tasks (i.e. object detection and image classification)
ModelTypes.GENERATIVE_LLM
Models that use vast amounts of data to generate human-like language and perform a wide range of natural language processing tasks
​Object Detection​
ModelTypes.OBJECT_DETECTION
Object detection models identify and locate objects within images or videos by assigning them specific bounding boxes

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
)
​