Utils.Types.ModelTypes

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
Binary Classification	`ModelType.BINARY_CLASSIFICATION`	Binary classification models predict only two categorical values, typically represented as 0 or 1
Multi Class	`ModelType.MULTI_CLASS`	Multiclass models predict multiple categorical values
Ranking	`ModelType.RANKING`	Ranking models predict the relative ordering of a set of items based on their features
Natural Language Processing (NLP)	`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)
Computer Vision (CV)	`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)
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
)

API reference

API clients

View source on Github

Model Types

Method

Code Example

View source on Github

​Model Types

​Method

​Code Example

Model Types

Method

Code Example