GNU GENERAL PUBLIC LICENSE
                      Version 3, 29 June 2007

    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 statistical learning theory (SLT) that provide tools for studies and allow to prove issues such as the guarantee of learning of a certain algorithm. Reference: "Measuring the Shattering coefficient of Decision Tree models"  
    Copyright (C) 2021  Martinelli, Igor.

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.

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