Metadata-Version: 1.2
Name: scikit-garden-forked
Version: 0.0.5
Summary: A garden of scikit-learn compatible trees, and I had few modifications to it.
Home-page: https://github.com/Demangio/scikit-garden
Maintainer: Guillaume
Maintainer-email: guillaumedemange@free.fr
License: new BSD
Description: # Scikit-Garden
        
        Forked from https://github.com/scikit-garden/scikit-garden
        
        
        Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.
        
        
        
        ## Installation
        
        Scikit-Garden depends on NumPy, SciPy, Scikit-Learn and Cython. So make sure these dependencies are installed using pip:
        
        ``
        pip3 install setuptools numpy scipy scikit-learn cython
        # or
        pip3 install -r requirements.txt
        ``
        
        After that my Scikit-Garden fork can be installed using pip.
        
        ``
        pip install git+https://git@github.com/Demangio/scikit-garden.git
        ``
        
        ## Available models
        
        ### Regressors
        
        * ExtraTreesRegressor (with `return_std` support)
        * ExtraTreesQuantileRegressor
        * RandomForestRegressor (with `return_std` support)
        * RandomForestQuantileRegressor
        
        
        ## Usage
        
        The estimators in Scikit-Garden are Scikit-Learn compatible and can serve as a drop-in replacement for Scikit-Learn's trees and forests.
        
        ``
        from sklearn.datasets import load_boston
        X, y = load_boston()
        
        
        ### Use QuantileForests for quantile estimation
        from skgarden import RandomForestQuantileRegressor
        rfqr = RandomForestQuantileRegressor(random_state=0)
        rfqr.fit(X, y)
        y_mean = rfqr.predict(X)
        y_median = rfqr.predict(X, 50)
        ``
        
        ## What changes in this release
        
        Change default predict method to the same as QuantReg package. This version is faster and include parametric estimation. 
        Adaptation of code to higher versions of dependencies. 
        
        ## Important links
        -  API Reference: https://scikit-garden.github.io/api/
        -  Examples: https://scikit-garden.github.io/examples/
        -  Modifications source: https://stackoverflow.com/questions/51483951/quantile-random-forests-from-scikit-garden-very-slow-at-making-predictions
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
