Illustrate the working of Descision Tree Algorithm with the help of suitable example

Sure, let's understand the working of the Decision Tree Algorithm with a simple example.

Suppose we have a dataset of weather conditions and we want to predict if a game can be played or not based on these conditions. The dataset is as follows:

Step 1: Calculate the entropy of the target.

Step 2: The dataset is then split on the different attributes. The entropy for each branch is calculated. Then it is added proportionally, to get total entropy for the split. The resulting entropy is subtracted from the entropy before the split. The result is the Information Gain, or decrease in entropy.

Step 3: Choose attribute with the largest information gain as the decision node, divide the dataset by its branches and repeat the same process on every branch.

Step 4: A branch with entropy of 0 is a leaf node.

Step 5: A branch with entropy more than 0 needs further splitting.

Step 6: The Decision Tree is built, and prediction is made with the help of this tree.

The decision tree for the above data will look something like this:

Outlook
|--- Sunny
|    |--- Humidity
|    |    |--- High: No
|    |    |--- Normal: Yes
|--- Overcast: Yes
|--- Rainy
|    |--- Windy
|    |    |--- False: Yes
|    |    |--- True: No

This tree can be used to predict whether a game can be played or not based on the weather conditions.