Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM
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Updated
Jul 14, 2021 - Python
Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM
Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost.
An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.
This repository contains code used for the numerical experiments in the Supervised Learning for Integrated Forecasting and Inventory Control paper by Joost F. van der Haar, Arnoud P. Wellens, Robert N. Boute and Rob J.I. Basten.
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