Metadata-Version: 2.1
Name: shatteringdt
Version: 0.1.0
Summary: Useful tools to work with Statistical Learning Theory (SLT) over scikit-learn decision trees.
Home-page: https://github.com/martinelligor/shatteringdt
Author: Igor Martinelli
Author-email: igor.martinelli03@gmail.com
License: GNU General Public License v3
Description: Useful tools to work with Statistical Learning Theory (SLT) over scikit-learn decision trees.
        
        # Description
        
        Learning, in Machine Learning (ML) area, is one of the most important steps in the construction of algorithms that seek to predict a certain task, whether this is the classification of objects, the forecast of demand for a specific product or even the diagnosis of malignant diseases. In ML, we can study supervised (which have a label, e.g., a class) and unsupervised algorithms, used for tasks such as pattern detection, grouping, among others that do not depend directly on a label. Knowing this, the present work aims to carry out the study of different supervised learning algorithms, in this case, the classification algorithms, more specifically Decision Trees, to carry out an analytical study about the steps that make up the learning process of the algorithm, exploring concepts of the SLT that provide tools for studies and allow to prove issues such as the guarantee of learning of a certain algorithm. Reference: Rodrigo Fernandes de Mello, Chaitanya Manapragada, Albert Bifet: "Measuring the Shattering coefficient of Decision Tree models". Expert Syst. Appl. 137: 443-452 (2019)
        
        It consists of two main modules:
        
        # Installation
         
        ## Dependencies
        
        - Python (>= 3.6)
        - numpy
        - tqdm
        - sklearn
        - matplotlib
        
        ## User installation
        
        ```bash
        pip install shatteringdt
        ```
        
        # About this work
        
        This project is a result of a Master's discipline of Institute of Mathematics and Computer Sciences (ICMC) of University of Sao Paulo (USP)
Keywords: Machine Learning,Shattering Coefficient,Chernoff Bound,Decision Tree,Statistical Learning Theory
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
