Explain Decision Tree based Classification Approach with example.

A Decision Tree is a simple representation for classifying examples. It is a Supervised Machine Learning 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.

Here is a step-by-step explanation of the Decision Tree based Classification Approach:

1. Data Collection: The first step in the process is gathering data. This data will be used to make predictions. The data is usually collected in a structured format like a CSV file, a database, or an Excel file.

2. Data Preparation: The next step is preparing the data for use in the decision tree algorithm. This involves cleaning the data to remove any errors or inconsistencies, and then splitting the data into features and targets. Features are the variables that will be used to predict the target, which is the outcome we are interested in predicting.

3. Building the Decision Tree: The decision tree is built by splitting the data into subsets based on different conditions. The goal is to create subsets that are as pure as possible, meaning they contain only one type of target value. The condition used to split the data is chosen based on the feature that provides the most information gain.

4. Making Predictions: Once the decision tree has been built, it can be used to make predictions. This is done by starting at the root of the tree and following the branches that correspond to the conditions that are met by the new data point. The prediction is the target value of the leaf node that is reached.

For example, let's say we have a dataset of patients with different symptoms and the goal is to predict whether they have a certain disease. The features could be the different symptoms, and the target would be whether or not they have the disease. The decision tree might first split the data based on whether the patient has a fever, then based on whether they have a cough, and so on. The final prediction would be based on the path through the tree that matches the patient's symptoms.