Increasing Trees in a Random Forest

Requirements Clarification & Assessment

1. Understanding the Question:

   • The primary query is whether the accuracy of a Random Forest model consistently improves with the incremental addition of more trees.

2. Key Concepts:

   • Random Forest: An ensemble learning method using multiple decision trees.
   • Accuracy: A measure of how well the model predicts outcomes.
   • Diminishing Returns: The point where additional trees provide minimal accuracy improvements.
   • Computational Cost: The resources required to train and evaluate the model.

3. Assumptions:

   • The dataset is large enough to require ensemble methods.
   • The model's performance is measured using standard accuracy metrics.
   • Computational resources are limited but sufficient for initial model training.

4. Potential Limitations:

   • Overfitting concerns, though mitigated by random forest's structure.
   • Increased computational time and resources with more trees.

5. Goals:

   • Determine the impact of adding more trees on model accuracy.
   • Identify the optimal number of trees balancing accuracy and computational cost.