Understanding decision trees
A decision tree is a very good example of "divide and conquer". It is one of the most practical and widely used methods for inductive inference. It is a supervised learning method that can be used for both classification and regression. It is non-parametric and its aim is to learn by inferring simple decision rules from the data and create such a model that can predict the value of the target variable.
Before taking a decision, we analyze the probability of the pros and cons by weighing the different options that we have. Let's say we want to purchase a phone and we have multiple choices in the price segment. Each of the phones has something really good, and maybe better than the other. To make a choice, we start by considering the most important feature that we want. And as such, we create a series of features that it has to pass to become the ultimate choice.
In this section, we will learn about:
Decision trees
Entropy measures
Random forests
We will also learn about...
