Random Forest Algorithm

Introduction

• The Random Forest algorithm is used to solve decision tree problems.
• It creates multiple decision trees on the same dataset.
• "Forest" means we are creating a collection of many decision trees.

Ensemble Learning

• Random Forest is an ensemble learning technique.
• Ensemble means we don't draw conclusions based on a single decision tree, but rather on a group of trees.
• Just like voting is done to pass a bill in India.
• Different learning methods combine to produce the output in Random Forest.

Overfitting Problem

• Overfitting occurs when the model gives very good results on the training data but performs poorly on the test data.
• The problem of overfitting is less in Random Forest because it does not rely on a single decision tree.
• It makes more accurate predictions.

Usage of Random Forest

• Random Forest can be used in both classification and regression.
• It generally provides higher accuracy in classification.

Working Methodology

Step 1: Data Bootstrapping

• Randomly select data from the original dataset, allowing repetition.
• Example: If you have 300 emails, you randomly select 100 emails from them.

Step 2: Creating Decision Trees

• Multiple decision trees are created from the selected dataset.
• For each decision tree, some features (attributes) are randomly selected.

Step 3: Calculating Output

• The output (like Spam or Not Spam) from each decision tree is calculated.
• The output is taken as the final result based on majority voting.

Usage in Regression

• If results in regression are obtained, like 10, 20, and 15, their average (mean) is calculated to give the final result.

Conclusion

• Random Forest is a powerful technique that uses a group of different decision trees to improve accuracy.
• It also reduces the problem of overfitting.
• It can be used in both classification and regression.

This is a complete overview of Random Forest. Thank you!