Normalize Nerd Lecture Notes: Random Forest Algorithm

Introduction

• Overview of the Random Forest algorithm
• Comparison with Decision Trees
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Dataset

• Small dataset used: 6 instances, 5 features
• Binary classification problem (target variable y: 0 and 1)

Decision Trees

• Definition: Splits the dataset recursively using decision nodes until reaching pure leaf nodes
• Best split found by maximizing entropy gain
• Process of decision-making:
  • If condition satisfied at decision node, move to left child
  • If not, move to right child
  • Reach leaf node for class label assignment

Issues with Decision Trees

• Sensitivity to training data: can lead to high variance
• Example: Changing one data sample can lead to a completely different tree

Introduction to Random Forest

• Definition: Collection of multiple random decision trees
• Less sensitive to training data due to ensemble approach
• Name "random" derives from the use of random processes in its creation

Building a Random Forest

1. Data Preparation: Create new datasets from the original data

   • Build 4 new datasets using random sampling with replacement (bootstrapping)
   • Each dataset has the same number of rows as the original
   • Example: Row IDs show repeats due to replacement

2. Training Decision Trees:

   • Train a decision tree on each bootstrap dataset
   • Use a randomly selected subset of features for each tree
   • Example of feature subsets used for training trees

3. Making Predictions:

   • Pass a new data point through each tree
   • Note down predictions from each tree
   • Combine predictions using majority voting (aggregation)
   • Example: Prediction is 1 based on majority vote

Important Concepts

• Bootstrapping:
  • Helps to reduce sensitivity to the original training data
• Random Feature Selection:
  • Reduces correlation between trees
  • Some trees may be trained on less important features, balancing out bad predictions

Feature Subset Size

• Ideal size of feature subset: close to the square root of total features
• Recommended values: close to log or square root of total features

Application to Regression Problems

• For regression: Combine predictions by taking the average

Conclusion

• Summary of Random Forest algorithm concepts
• Encouragement to share and subscribe for more content
• Thank you for watching!