Metadata-Version: 2.1
Name: cortado
Version: 1.0rc3
Summary: High performance ML library with ultra fast XGBoost implementation in pure Python
Home-page: https://github.com/Statfactory/cortado
Author: Adam Mlocek
License: MIT
Description: # cortado: high performance 100% Python package for machine learning
        
        ## Installation
        
        *cortado* can be installed from pip:
        
        ```
        pip install cortado
        ```
        
        ## Main features:
        * native support for both numeric and categorical data (*covariates* and *factors*)
        * innovative feature engineering: virtual data columns and easy conversions between numeric and categorical data
        * out of core data processing when dataframes are bigger than RAM
        * implementation of XGBoost logistic in 500 lines of Python code, 3x faster than original C++ implementation (using Numba jit under the hood)
        * easy to extend, written in functional style for easy composition
        * works well with pandas dataframes
        * more to come soon!
        
        ## Demo notebooks on Kaggle:
        * [Quick start](https://www.kaggle.com/neostat/cortado-quick-start)
        * [Cortado vs XGBoost](https://www.kaggle.com/neostat/cortado-vs-xgboost)
        * [Factors and covariates](https://www.kaggle.com/neostat/cortado-factors-and-covariates)
        * [Out of core processing](https://www.kaggle.com/neostat/cortado-out-of-core)
        
        ## How to contribute
        
        All contributions and bug reports are welcome.
Platform: UNKNOWN
Description-Content-Type: text/markdown
