Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
Dependencies if any, must be appropriately added. Test run of the train function on a regression problem must pass, and the function must attempt to train an HistGradientBoostingRegressor as a potential best fit model.
Add HistGradientBoostingRegressorinto the library.
Primary File to Change: https://github.com/blobcity/autoai/blob/main/blobcity/config/regressor_config.py
Reference HistGradientBoostingRegressor Implementation: https://github.com/blobcity/ai-seed/blob/main/Regression/Histogram-Based%20Gradient%20Boosting%20Trees/HistGradientBoostingRegressor.ipynb
Official API Refer for parameters: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor
Dependencies if any, must be appropriately added. Test run of the train function on a regression problem must pass, and the function must attempt to train an HistGradientBoostingRegressor as a potential best fit model.