A decision tree is a supervised learning method for predicting the output of a target variable, effective in both classification and regression problems. It uses the iterative dichotomiser 3 (ID3) algorithm to calculate information gain for features to optimize class separation and build nodes until all features are used or leaf nodes remain. Key concepts include entropy, which measures disorder, and information gain, which indicates the effectiveness of features in classification.

2. What is a Decision Tree?
A decision tree is a tree-based supervised learning method used to predict the output of a target
variable.
Decision Tree
Determine
Odds
Classificatio
n
Problems
Regression
Problems

3. How Does a Decision Tree Work?

4. How Does a Decision Tree Work?

5. Iterative Dichotomiser 3 Example
Calculate the Information Gain of each feature.
Split the dataset SSS using the feature with the
highest Information Gain to best separate the
classes.
Make a decision tree node using the feature with the
maximum Information gain.
If all rows belong to the same class, make the
current node as a leaf node with the class as its
label.
Continue the process for the remaining features
until all features are used, or the decision tree has
only leaf nodes.
ID3 Algorithm Steps
Covid-19 Infection

6. Iterative Dichotomiser 3 Example
Entropy measures disorder, and in a
dataset, it quantifies the disorder within the
target feature.
Entropy
Information Gain measures how much a
feature reduces entropy, indicating its
effectiveness in classifying the target
classes. The feature with the highest
Information Gain is chosen as the best.
Information Gain

7. Iterative Dichotomiser 3 Example

8. Iterative Dichotomiser 3 Example

9. Iterative Dichotomiser 3 Example

10. Iterative Dichotomiser 3 Example