Random Forest Lecture Notes

Introduction

• Discussion on Random Forest algorithm.
• Focus on understanding through a code example.
• Benefits of Random Forest include performance without high parameter tuning.

Dataset Overview

• Using a famous heart disease dataset.
• Contains data for approximately 2003 patients.
• Features include age, biological metrics, and heart disease status.
• Objective: Predict whether a patient has heart disease or not.

Data Preparation

• Data is split into training and testing sets:
  • 242 rows for training.
  • 61 rows for testing.
• Random Forest object created and model trained.

Comparison with Other Algorithms

• Comparison of Random Forest performance with:
  • Decision Trees
  • Logistic Regression
  • SVC (Support Vector Classification)
• Random Forest often performs well against these algorithms.

Hyperparameter Tuning

• Random Forest has around 25 hyperparameters.
• Tuning these parameters can improve model performance.
• Example of tuning:
  • Adjusting the number of trees in the model.
  • Resulted in performance increase to approximately 0.84 accuracy.

Hyperparameter Tuning Methodology

• Use Grid Search for hyperparameter tuning:
  • Test multiple values for each hyperparameter.
  • Create combinations for training models.
  • Example: 4 parameters with 3 values = 108 combinations.
• Train a Random Forest for each combination to find the best parameters.

Randomized Search CV

• When working with large datasets, consider using Randomized Search CV:
  • Selects a random subset of combinations to evaluate.
  • Faster than comprehensive Grid Search.
  • Provides reasonable results quickly, but may not always find the best configuration.

When to Use Each Method

• Grid Search:
  • Best for smaller datasets with fewer parameters.
  • Achieves high accuracy but can be time-consuming.
• Randomized Search:
  • Best for larger datasets with many hyperparameters.
  • Offers quicker results and relatively good accuracy.

Conclusion

• Random Forest is effective for classification tasks.
• Always consider hyperparameter tuning for improved performance.
• Choose the right tuning method based on dataset size and complexity.