# Decision Trees for Classification: A Machine Learning Algorithm

- There are two main types of Decision Trees: – – What we’ve seen above is an example of classification tree, where the outcome was a variable like ‘fit’ or ‘unfit’.
- Information gain is also called as Kullback-Leibler divergence denoted by IG(S,A) for a set S is the effective change in entropy after deciding on a particular attribute A.
- where IG(S, A) is the information gain by applying feature A. H(S) is the Entropy of the entire set, while the second term calculates the Entropy after applying the feature A, where P(x) is the probability of event x. – – Let’s understand this with the help of an example…
- We can clearly see that IG(S, Outlook) has the highest information gain of 0.246, hence we chose Outlook attribute as the root node.
- Here we observe that whenever the outlook is Overcast, Play Golf is always ‘Yes’, it’s no coincidence by any chance, the simple tree resulted because of the highest information gain is given by the attribute Outlook.

Our blog introduces you to Decision Trees, a type of supervised machine learning algorithm that is mostly used in classification problems.

@kdnuggets:

Decision Trees for Classification: A #MachineLearning Algorithm

Introduction

Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split.

An example of a decision tree can be explained using above binary tree. Let’s say you want to predict whether a person is fit given their information like age, eating habit, and physical activity, etc. The decision nodes here are questions like ‘What’s the age?’, ‘Does he exercise?’, ‘Does he eat a lot of pizzas’? And the leaves, which are outcomes like either ‘fit’, or ‘unfit’. In this case this was a binary classification problem (a yes no type problem).

There are two main types of Decision Trees:

What we’ve seen above is an example of classification tree, where the outcome was a variable like ‘fit’ or ‘unfit’. Here the decision variable is Categorical.

Here the decision or the outcome variable is Continuous, e.g. a number like 123.

Working

Now that we know what a Decision Tree is, we’ll see how it works internally. There are many algorithms out there which construct Decision Trees, but one of the best is called as ID3 Algorithm. ID3…

Decision Trees for Classification: A Machine Learning Algorithm

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