0.3.0a2 (2026-03-10)

  • Added use_gpu parameter to classification and regression advanced options docs

  • Added foundation_model_path parameter to LazyForecaster for offline/air-gapped environments

  • Added CatBoost to boost install extras (pip install lazypredict[boost])

  • Updated all documentation examples to include GPU acceleration and local model weights

  • Fixed Sphinx documentation build pipeline (RST formatting, myst_parser migration)

  • Updated ReadTheDocs and GitHub Actions docs workflow configuration

0.3.0a1 (2026-03-10)

  • New: Time Series Forecasting — LazyForecaster benchmarks 26+ forecasting models in one call

  • Statistical models: Naive, SeasonalNaive, SimpleExpSmoothing, Holt, HoltWinters (additive & multiplicative), Theta, SARIMAX, AutoARIMA

  • ML models: LinearRegression, Ridge, Lasso, ElasticNet, KNN, SVR, DecisionTree, RandomForest, GradientBoosting, AdaBoost, Bagging, ExtraTrees, XGBoost, LightGBM, CatBoost

  • Deep learning models: LSTM, GRU (via PyTorch)

  • Foundation model: Google TimesFM 2.5 (200M-parameter zero-shot pretrained transformer)

  • GPU acceleration via use_gpu=True: XGBoost (device="cuda"), LightGBM (device="gpu"), CatBoost (task_type="GPU"), cuML (RAPIDS) auto-discovery, LSTM/GRU/TimesFM CUDA device placement

  • Automatic seasonal period detection via autocorrelation (ACF)

  • Exogenous variable support for SARIMAX, AutoARIMA, and ML models

  • Cross-validation with expanding window (TimeSeriesSplit)

  • New forecasting metrics: MAPE, SMAPE, MASE

  • New install extras: pip install lazypredict[timeseries], [deeplearning], [foundation]

  • Added categorical_encoder parameter to LazyClassifier and LazyRegressor

  • Added CatBoost models (CatBoostClassifier, CatBoostRegressor) to supervised and time series

  • Added cuML (RAPIDS) GPU-native scikit-learn model auto-discovery when use_gpu=True

  • Refactored Supervised.py with type hints, logging, and input validation

0.2.15 (2025-04-06)

  • Added MLflow integration for experiment tracking

  • Added support for Python 3.13

  • Updated all dependencies to latest versions

  • Improved model logging and tracking capabilities

  • Added automatic model signature logging with MLflow

0.2.11 (2022-02-06)

  • Updated the default version to 3.9

0.2.10 (2022-02-06)

  • Fixed issue with older version of Scikit-learn

  • Reduced dependencies sctrictly to few

0.2.8 (2021-02-06)

  • Removed StackingRegressor and CheckingClassifier.

  • Added provided_models method.

  • Added adjusted r-squared metric.

  • Added cardinality check to split categorical columns into low and high cardinality features.

  • Added different transformation pipeline for low and high cardinality features.

  • Included all number dtypes as inputs.

  • Fixed dependencies.

  • Improved documentation.

0.2.7 (2020-07-09)

  • Removed catboost regressor and classifier

0.2.6 (2020-01-22)

  • Added xgboost, lightgbm, catboost regressors and classifiers

0.2.5 (2020-01-20)

  • Removed troublesome regressors from list of CLASSIFIERS

0.2.4 (2020-01-19)

  • Removed troublesome regressors from list of REGRESSORS

  • Added feature to input custom metric for evaluation

  • Added feature to return predictions as dataframe

  • Added model training time for each model

0.2.3 (2019-11-22)

  • Removed TheilSenRegressor from list of REGRESSORS

  • Removed GaussianProcessClassifier from list of CLASSIFIERS

0.2.2 (2019-11-18)

  • Fixed automatic deployment issue.

0.2.1 (2019-11-18)

  • Release of Regression feature.

0.2.0 (2019-11-17)

  • Release of Classification feature.

0.1.0 (2019-11-16)

  • First release on PyPI.