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Awesome GitHub RepositoriesNumerical Split Optimizations
Adjusting the quantization and split points of numerical features to improve model precision.
Distinct from Numerical Quantization: Candidates focus on data type precision or rounding, whereas this is about decision tree split quantization.
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Awesome Numerical Split Optimizations GitHub Repositories
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- catboost/catboost
catboost/catboost
8,808Auf GitHub ansehenCatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
Optimizes the number of splits for numerical features to increase precision for high-impact variables.
C++big-datacatboostcategorical-features
