fabsig/KTBoost
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
This tool helps data analysts and researchers build predictive models for a wide range of data types. You input your dataset with features and target variables, and it outputs a trained model that can predict outcomes or classify categories. This is ideal for professionals in fields like finance, healthcare, or marketing who need to make accurate forecasts or identify patterns from complex data.
No commits in the last 6 months. Available on PyPI.
Use this if you need to build robust regression or classification models and want flexibility in choosing loss functions, base learners (trees, kernel functions, or a combination), and optimization methods.
Not ideal if you prefer simple, highly interpretable models, or if you are not comfortable working with advanced machine learning concepts.
Stars
63
Forks
20
Language
Python
License
—
Category
Last pushed
Nov 22, 2021
Commits (30d)
0
Dependencies
3
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