Before digging into the decision tree algorithm implementation, let us first understand the decision tree definition. A decision tree is a powerful prediction methodology that can be leveraged for operational use. Decision tree implementation offers a great foundation so that you can make a calculated decision by weighing out the possible consequences. Don’t let the decision tree definition daunt you as the decision tree concept is quite interesting and simple. Let us explore more on decision trees. Let us take a simple example of selecting your flight for your next trip. How would you go about it? Check for flights on the available date. If the flights are not available, then look at another date. If the flights are available, then check for the duration of flights and direct flights. You would also look at the budget (if that is the constraint). If it is well within the budget, you will book the flight, else you will look for another flight. This could be charted out into a decision tree. The steps for you to book a flight will be available at a glance with decision trees. We can extend the validity of decision tree implementations and other machine learning algorithms even in our personal lives – and not just the professional life.

## What is Decision Tree Algorithm?

The best way you can relate to a decision tree is if you can picture a flowchart. Each node in such a flowchart indicates a test on the feature. Each leaf node represents the decision post the computation of all the features. The branches represent the conjunction of the features that leads to the possible decision in the flowchart. The paths that connect the root to the leaf indicate the classification rule. Let’s consider the below flowchart for decision tree algorithm implementation:

Now, you may ask how a decision tree is constructed. This is done using an algorithmic approach that recognises and sets different conditions to split the existing data set. This method is not only practical but is also quite widely used in classification as well as regression tasks. Before you get confused, we will throw some light on these terms.

A tree model comes with a target variable that can accept values. In a classification tree implementation, the target variable of the tree model takes a discrete set of values. Whereas, in a regression tree implementation, the target variable of the tree model takes continuous values that are generally real numbers.

## An Approach to Decision Tree Implementation

The approach that is used while making a decision tree is to ask different questions at each node. On the basis of the question, the information gain is calculated.

#### 1. Information Gain

Decision tree algorithm implementation uses information gain to decide which feature needs to be split in the next step. To keep the decision tree simple, you need to ensure that the tree is small. And, with the huge amount of data that you have, this might seem daunting. To construct the decision tree, you need to measure how much information a feature can give us. We can term this as information value. The highest information node will be the first split node and the process continues until the information gain for the node is zero.

#### 2. Gini Impurity

To keep the decision tree simple, the information present must be pure. Now, let’s see what this means. You can call a dataset pure if it comprises data that belongs to the same class. You can term a dataset impure if it features data that is a mix of different classes. Now, you need to consider how likely is it that a new instance of a random variable is correctly classified? The likelihood of this incorrect classification is what is Gini Impurity. How likely is the incorrect classification if the dataset is pure? You could say zero. But, on the other hand, the classification of a mixture of datasets can be quite inaccurate. While the above two things are only a part of the decision-making process. There are so many aspects that go into building a decision tree and calculating what is the best scenario. Decision trees are important predictive modelling approaches used in data mining, statistics and machine learning. They offer great ease-of-use and are quite easy to understand. They are known to be stable while handling categorical as well as numerical data. And, they need minimal preprocessing as they are quite resistant to outliers.

## Get to Know More on Machine Learning Algorithms

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