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.

insurance-causal

Causal inference for insurance pricing: double machine learning, price elasticity, heterogeneous treatment effects, and post-hoc rate change evaluation for UK personal lines.

py-automl-lib

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

hypergbm

A full pipeline AutoML tool integrated various GBM models

insurance-quantile

Actuarial tail risk quantile and expectile regression for UK personal lines pricing, including EQRN extreme quantile neural nets (Pasche & Engelke 2024)

shap-relativities

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

hyperparameter-hunter

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

insurance-distill

Distil GBM models into multiplicative GLM factor tables for insurance rating engines.

boost-loss

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

insurance-severity

Insurance severity modelling: composite models, DRN, EVT, MDN, SPQRx, tail scoring, CMRS allocation, Projection-to-Ultimate, and AIPW IBNR reserving

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-demand

Deprecated — use insurance-optimise instead

retrofit

Simple, opinionated machine learning workflows for rapid iteration.

pandasflow

A set of custom python modules for friendly workflow on pandas

insurance-distributional

Distributional GBM for insurance pricing — Tweedie, ZIP, Gamma, Negative Binomial with CatBoost

timber-compiler

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

insurance-cv

Temporal and distributional cross-validation, and feature screening, for insurance pricing models

insurance-deploy

Champion/challenger pricing framework for UK insurance — model registry, quote routing, ENBP audit logging, and statistical promotion tests

tsforecasting

TSForecasting: Automated Time Series Forecasting Framework

insurance-thin-data

Foundation models and transfer learning for thin-data insurance pricing segments