Welcome to the Decision Trees Course! 🌳

Implementation Overview

Decision trees are powerful tools used for classification and regression tasks. They are intuitive, non-parametric models that mimic human decision-making processes.

Steps to Implement a Decision Tree:

• Identify the root node based on the best feature split.
• Recursive splitting to further partition the data.
• Prune the tree to avoid overfitting.

Advantages of Decision Trees:

• Easy to understand and interpret.
• Requires little data preprocessing.
• Handles categorical and numerical data.

Potential Challenges:

• Overfitting, especially when working with complex trees.
• Sensitive to outliers and noisy data.

Continue Learning!

Check out other topics:

• Linear Regression
• Logistic Regression
• Support Vector Machines

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