Metadata-Version: 1.1
Name: pylearning
Version: 2.0.0b1
Summary: Simple high-level library to use decision trees and random forest learners
Home-page: https://github.com/amstuta/pylearning.git
Author: Arthur Amstutz
Author-email: arthur.amstutz@gmail.com
License: MIT
Description: ## Pylearning: a Python library to use decision trees and random forest learners
        
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/amstuta/pylearning/blob/master/LICENSE.md)
        
        Pylearning is a high-level machine learning package designed to easily prototype
        and implement data analysis programs.
        
        The library includes four algorithms:
        - Decision tree classifier
        - Random forest classifier
        - Decision tree regressor
        - Random forest regressor
        
        The two random forests algorithms use multithreading to train the trees in a
        parallelized fashion.
        This package is compatible with Python3+.
        
        ### Basic usage
        
        All the algorithms available use the same simple interface described in the
        examples below.
        
        ```python
        # Basic classification example using a decision tree
        
        from pylearning.trees import DecisionTreeClassifier
        
        # Load your training dataset
        features, targets = ...
        
        tree = DecisionTreeClassifier(max_depth=10)
        tree.fit(features, targets)
        
        # Load a testing sample
        test_feature, test_target = ...
        
        predicted_class = tree.predict(test_feature, test_target)
        ```
        
        ```python
        # Basic regression example using a random forest
        
        from pylearning.ensembles import RandomForestRegressor
        
        # Load the training dataset
        features, targets = ...
        
        rf = RandomForestRegressor(nb_trees=10, nb_samples=100, max_depth=20)
        rf.fit(features, targets)
        
        # Load a testing sample
        test_feature, test_target = ...
        
        value_predicted = rf.predict(test_feature, test_target)
        ```
        
        ### Installation
        
        Pylearning has no external dependencies. It can be installed simply using Pypy:
        ```sh
        pip3 install pylearning
        # OR
        pip install pylearning
        ```
        
        ### Further improvements
        
        The core functionalities of trees and random forest are implemented in this
        project, however there are many improvements that could be added:
        - gini criterion for splitting nodes
        - pruning
        - ability to split a node into an arbitrary number of child nodes
        - optimizations to reduce time and memory consumption
        - ...
        
        If you wish, you're welcome to participate in the project or to make suggestions !
        To do so, you can simply open an issue or fork the project and then create a pull
        request.
        
Keywords: machine learning data decision trees random forest
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
