recurse

list. A list containing the tree nodes.

nodes

int. The left child of the parent node.

left

int. The right child of the parent node.

right

int. The current node being analyzed

node

parent

list. The number of dataset samples for each node of the tree.

samples

string. The type of the node, e.g., leaf if it's a leaf node.

node_type

float. The shattering coefficient that are begin computed.

shattering

int. The # of samples to consider in dataset.

n_samples

Using a recursion, calculates the shattering coefficient
for a rpart decision tree.

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: Rodrigo Fernandes de Mello, Chaitanya Manapragada, Albert Bifet: "Measuring the Shattering coefficient of Decision Tree models". Expert Syst. Appl. 137: 443-452 (2019)<DOI:10.1016/j.eswa.2019.07.012>.

recurse: Calculates the shattering coefficient for a decision tree.

Description

Usage

Arguments

Value