orchardbirds/bokbokbok
Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM
This tool helps data scientists and machine learning engineers fine-tune their XGBoost and LightGBM models for specific business problems. It allows you to input your model training data and apply specialized loss functions and evaluation metrics, resulting in models that are more accurately aligned with your project goals, particularly for classification or regression tasks. This is ideal for those who build and deploy predictive models.
No commits in the last 6 months. Available on PyPI.
Use this if you are a data scientist or machine learning engineer who needs more control over how your XGBoost or LightGBM models learn and are evaluated, especially when standard metrics don't fully capture your problem's nuances.
Not ideal if you are looking for a no-code solution or are not comfortable working with Python and machine learning model development.
Stars
39
Forks
7
Language
Python
License
MIT
Category
Last pushed
Jul 22, 2025
Commits (30d)
0
Dependencies
3
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/orchardbirds/bokbokbok"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
HelmchenLabSoftware/Cascade
Calibrated inference of spiking from calcium ΔF/F data using deep networks
adobe/antialiased-cnns
pip install antialiased-cnns to improve stability and accuracy
KaiyangZhou/pytorch-center-loss
Pytorch implementation of Center Loss
devsisters/pointer-network-tensorflow
TensorFlow implementation of "Pointer Networks"
kimhc6028/relational-networks
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)