Classical ML & Gradient Boosting

Industry-standard library for traditional machine learning (classification, regression, clustering, pipelines).

Scalable, high-performance gradient boosting library. Still dominates Kaggle and tabular competitions.

Microsoft's ultra-fast gradient boosting framework, optimized for speed and memory.

Gradient boosting that handles categorical features natively with great out-of-the-box performance.

Unified framework for machine learning with time series. scikit-learn compatible API for forecasting, classification, clustering, and anomaly detection.

Lightning-fast statistical forecasting with ARIMA, ETS, CES, and Theta models. Optimized for high-performance time series workloads.

Scalable machine learning for time series forecasting. Train any sklearn-compatible model on millions of time series with efficient feature engineering. Apache 2.0 licensed.

GPU-accelerated machine learning algorithms with scikit-learn compatible API. 10-50x faster than CPU implementations for large datasets. Apache 2.0 licensed.

Distributed machine learning on Apache Spark. Scalable, composable APIs for text analytics, vision, anomaly detection with seamless Python/Scala/R/.NET integration. MIT licensed.

User-friendly forecasting and anomaly detection for time series. Unifies classical statistical models (ARIMA, ETS) with modern neural networks (N-BEATS, TFT, DeepAR) in a single scikit-learn compatible API. Apache 2.0 licensed.

Time series forecasting with PyTorch. Multiple neural architectures (N-BEATS, TFT, DeepAR) with in-built interpretation capabilities, built on PyTorch Lightning for distributed training. MIT licensed.