RektPunk/RektGBM
No-brainer model combining LightGBM and XGBoost with hyperparameter tuning using Optuna
This tool helps data scientists and machine learning engineers quickly build and optimize robust predictive models for their data. You input your training and test datasets along with the target variable, and it outputs predictions for your test data. It's designed for practitioners who need to deploy effective models without spending excessive time on model selection and fine-tuning.
No commits in the last 6 months. Available on PyPI.
Use this if you need to rapidly create a high-performing machine learning model for tasks like classification or regression, without getting bogged down in extensive hyperparameter optimization or choosing between gradient boosting frameworks.
Not ideal if you require deep, manual control over every aspect of model architecture and hyperparameter settings, or if you need to use models other than gradient boosting.
Stars
21
Forks
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Language
Python
License
MIT
Category
Last pushed
Nov 20, 2024
Commits (30d)
0
Dependencies
9
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