utils.types.ModelTypes
Arize enum to specify your model type represented in the platform and validate applicable performance metrics.
from arize.utils.types import ModelTypes
Specify a
model_type when logging a prediction for the first time for a new model.list_types()View Source
Returns a list of all model types.
ModelTypes.list_types()
Use Case | SDK ModelType | Description |
|---|---|---|
ModelTypes.REGRESSION | Regression models predict continuous values | |
ModelType.BINARY_CLASSIFICATION | Binary classification models predict only two categorical values, typically represented as 0 or 1 | |
ModelType.SCORE_CATEGORICAL | Multiclass models predict multiple categorical values | |
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 |
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
)
Last modified 1mo ago