This article will cover one of the most advanced algorithms and most widely used in analytical applications. This is an extensive subject, as we have several algorithms and various techniques for working with decision trees.
On the other hand, these algorithms are among the most powerful in Machine Learning and are easy to interpret. So, let’s start by defining what decision trees are and their representation through machine learning algorithms.
For decision tree learning models, we will study some algorithms with C4.5, C5.0, CART, and ID3. In addition, there are some specialized types of decision trees, and we will learn this following the chapter.
The main specialization of decision trees is RandomForest, which is nothing more than a collection of decision trees. We can use RandomForest for attribute selection, i.e., we can use decision trees for Machine Learning models themselves and apply feature selection techniques to prepare our dataset for other machine learning algorithms.
Finally, we will create models, make predictions, study the parameters and pre-processing details of decision trees, and interpret the results of predictive models.
When creating decision trees, we can have trees with lots of branches and leaves, and at some point, we will have to stop the construction of the tree or make adjustments to reduce the number of decision points in the predictive model.
Decision Trees
This machine learning technique is easy to interpret; that is, we can quickly solve the result of a Decision Tree model, RandomForest, or even an Ensemble method, unlike other techniques such as Artificial Neural Networks or Deep Learning challenging to interpret the result.
Decision Trees are known as one of the most powerful and widely used machine learning modeling techniques. Decision Trees can naturally induce rules that can be used for data classification or to make predictions.
A decision tree is a decision support tool. Graphically presents the shape of an upside-down tree, where the root is at the top, and the leaves are at the bottom.
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