Machine Learning with Decision Trees

Machine Learning with Decision Trees

Introduction

This paper shows you how to get started with machine learning by applying decision trees using Python on an established dataset. The code used in this article is available on Github. A popular library for creating decision trees is the standard scikit — learn and with this library you can get your first machine learning model running with just a few lines of computer code. In subsequent articles you will apply the SparkML library for machine learning.

Decision trees have influenced the development of machine learning algorithms, including Classification and Regression Tree (CART) models. The divide and conquer approach has attracted many to use them successfully.
A tree-like model of decisions is drawn that can be visually presented and saved to file, both in image form or in pseudo-code form.
A decision tree is drawn like an upside-down tree. We start from the root node, then split the nodes at each level until we reach leaf nodes which represent outcomes or decisions. At each of the internal nodes a decision is taken which then leads to further nodes.

Model From Iris Data

Figure 1 shows a decision tree for the famous Iris dataset. This dataset is available for download from the UCI website which has a list of hundreds of datasets for machine learning applications.

read more at:   https://dzone.com/articles/machine-learning-with-decision-trees-1

Posted by Alan, Tendron Systems Ltd