The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
The cvglmnet and related functions have been replaced with sklearn's LogisticRegression and GridSearchCV for model fitting and hyperparameter tuning. The choose_regularization function has been removed since sklearn handles regularization differently. The cv_glmnet_performance function has been replaced with cv_logistic_regression_performance to extract performance metrics from GridSearchCV. The weights_from_link function remains the same.
The rest of the code structure and functionality remains largely the same. This version should work without the need for glmnet_python and cvglmnet packages.
Using calude 3 opus
In this rewritten version:
The cvglmnet and related functions have been replaced with sklearn's LogisticRegression and GridSearchCV for model fitting and hyperparameter tuning. The choose_regularization function has been removed since sklearn handles regularization differently. The cv_glmnet_performance function has been replaced with cv_logistic_regression_performance to extract performance metrics from GridSearchCV. The weights_from_link function remains the same. The rest of the code structure and functionality remains largely the same. This version should work without the need for glmnet_python and cvglmnet packages.