Catboost Python Packages

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

skforecast

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

catboost-dev

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

hgboost

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.

boost-loss

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost

sklearn-utilities

Utilities for scikit-learn. Append prediction to x, append prediction to x single, append x prediction to x, compose var estimator, data frame wrapper, drop by noise prediction, drop missing rows y, dummy regressor var, estimator wrapper base, excluded column transformer pandas, feature union pandas, id transformer, included column transformer pand

insurance-causal

Causal inference for insurance pricing — DML, CatBoost nuisance models, confounding bias reports

hypergbm

A full pipeline AutoML tool for tabular data

retrofit

High-Performance ML Training, Scoring & Evaluation (Polars + GPU-Ready)

shap-relativities

SHAP-based rating relativities from GBM models for insurance pricing — extract GLM-style factors from CatBoost, Polars

insurance-quantile

Actuarial tail risk quantile/expectile regression for insurance pricing - TVaR, large loss loading, ILF curves, CatBoost

insurance-distill

GBM-to-GLM distillation for insurance pricing - surrogate factor tables for Radar/Emblem rating engines

insurance-severity

Severity modelling for insurance pricing - spliced distributions, DRN, composite regression, EQRN extreme quantiles

hyperparameter-hunter

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

pandasflow

A set of custom python modules for friendly workflow on pandas

insurance-demand

Deprecated — merged into insurance-optimise

insurance-cv

Temporal cross-validation for insurance pricing - respects policy time structure, CatBoost, Polars

tsforecasting

TSForecasting is an automated time series forecasting framework

insurance-thin-data

Transfer learning and foundation models for thin-segment pricing - GLMTransfer, TabPFN wrapper, MMD shift test

timber-compiler

Classical ML Inference Compiler — compiles XGBoost, LightGBM, sklearn, CatBoost, and ONNX models to optimized C99 / LLVM IR / WASM inference artifacts. Includes hardware acceleration (AVX2/NEON/CUDA), safety certification (DO-178C/ISO-26262), supply-chain signing, and embedded/ROS2/PX4 deployment.

insurance-distributional

Distributional GBM for insurance pricing — TweedieGBM, GammaGBM, ZIPGBM, NegBinomialGBM, per-risk volatility scoring

py-automl-lib

Python package for automated hyperparameter-optimization of common machine-learning algorithms

insurance-deploy

Champion/challenger pricing framework — shadow mode, SHA-256 routing, SQLite quote log, bootstrap LR test, ENBP audit (146 tests)