Fit interpretable models. Explain blackbox machine learning.

Generate Diverse Counterfactual Explanations for any machine learning model.

Lightweight Python implementation of SHAP

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

The machine learning toolkit for time series analysis in Python

A PyTorch implementation of learning shapelets from the paper Grabocka et al., „Learning Time-Series Shapelets“.

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Spark RAPIDS plugin - accelerate Apache Spark with GPUs

🤖🪝Interpretability with tensordict and torch hooks.

Model interpretability and understanding for PyTorch

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Deep Reinforcement Learning implementation for the VN2 Inventory Planning Challenge. We use Hindsight Differentiable Policy Optimization, as described in the paper Deep Reinforcement Learning for I…

Julia package for time series forecasting, inspired by R’s forecast package—part of the TAFS Forecasting Ecosystem.

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

Simplify ggplot2 visualisation

Python library for confidence sequences, sequential testing, e-processes, e-values, and game-theoretic probability.

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

A Library for Conformal Hyperparameter Tuning

cuDF - GPU DataFrame Library

Forecast evaluation library

SteosMorphy

Morphological analyzer / inflection engine for Russian and Ukrainian languages.

Code from the book Fighting Churn With Data

ForeCA: Forecastable Component Analysis in R

Forecastable Component Analysis (ForeCA) in Python

⚡ TabPFN: Foundation Model for Tabular Data ⚡

This repository holds the code developed during my Master theis