Yes. I would be willing to contribute this feature with guidance from the MLflow community.
Proposal Summary
For developing some metrics, one would need to use the output probability.
I propose to add class_probability to the input "eval_df" of "custom_metrics" function.
Motivation
What is the use case for this feature?
For some classification algorithms, I'd like to use precision_recall_auc as the primary metric however I cannot implement it, as it requires the output probability.
Why is this use case valuable to support for MLflow users in general?
In many imbalanced datasets, these metrics are the suggested metrics in the community.
Some Thoughts
For the estimators that do not support "predict_prob" these columns could be removed or set to None in the "eval_df" DF.
Currently, the precision_recall_auc or roc_auc values are automatically computed in the "evaluation" step if the estimator has some specifications, a similar approach can be used in the recipes.
Details
No response
What component(s) does this bug affect?
[ ] area/artifacts: Artifact stores and artifact logging
[ ] area/build: Build and test infrastructure for MLflow
Willingness to contribute
Yes. I would be willing to contribute this feature with guidance from the MLflow community.
Proposal Summary
For developing some metrics, one would need to use the output probability. I propose to add class_probability to the input "eval_df" of "custom_metrics" function.
Motivation
Details
No response
What component(s) does this bug affect?
area/artifacts
: Artifact stores and artifact loggingarea/build
: Build and test infrastructure for MLflowarea/docs
: MLflow documentation pagesarea/examples
: Example codearea/gateway
: AI Gateway service, Gateway client APIs, third-party Gateway integrationsarea/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models
: MLmodel format, model serialization/deserialization, flavorsarea/recipes
: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/projects
: MLproject format, project running backendsarea/scoring
: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra
: MLflow Tracking server backendarea/tracking
: Tracking Service, tracking client APIs, autologgingWhat interface(s) does this bug affect?
area/uiux
: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows
: Windows supportWhat language(s) does this bug affect?
language/r
: R APIs and clientslanguage/java
: Java APIs and clientslanguage/new
: Proposals for new client languagesWhat integration(s) does this bug affect?
integrations/azure
: Azure and Azure ML integrationsintegrations/sagemaker
: SageMaker integrationsintegrations/databricks
: Databricks integrations