Random Forest Model for Loan Action Prediction - Go Implementation

This is a Go implementation of a Random Forest classifier for predicting loan actions, converted from the original Python version using scikit-learn.

Features

• Custom Random Forest Implementation: Built from scratch without external ML libraries
• Decision Tree Algorithm: Complete implementation of decision trees with Gini impurity
• Bootstrap Sampling: Implements bagging for training diverse trees
• Feature Importance: Calculates and displays feature importance scores
• Cross-Validation: K-fold cross-validation for model evaluation
• Data Preprocessing: Basic CSV loading and data handling
• Model Evaluation: Accuracy calculation and confusion matrix

Key Differences from Python Version

Advantages of Go Implementation:

• Performance: Significantly faster execution due to Go's compiled nature
• Memory Efficiency: Better memory management and lower overhead
• Concurrency: Easy to parallelize tree training (can be added)
• Deployment: Single binary with no dependencies
• Type Safety: Compile-time error checking

Current Limitations:

• Data Preprocessing: Simplified compared to pandas functionality
• Visualization: No plotting capabilities (matplotlib equivalent needed)
• Model Persistence: No built-in model serialization (can be added)
• Statistical Functions: Basic implementations only

Usage

1. Prepare your data: Ensure you have three CSV files:

   • TrainingSet.csv
   • TestSet.csv
   • ValidationSet.csv

2. Run the program:

   go run main.go

3. Expected CSV format:

   • Must contain an action_taken column as the target variable
   • Categorical data will be automatically hashed to numeric values
   • Missing values are handled with simple strategies

Code Structure

Core Components

• Dataset: Represents a collection of features and labels
• RandomForest: Main model structure with multiple decision trees
• DecisionTree: Individual tree with recursive splitting logic
• TreeNode: Represents nodes in the decision tree

Key Functions

• loadAndExploreData(): Loads CSV files and performs basic data exploration
• preprocessData(): Handles data preprocessing (simplified)
• Train(): Trains the Random Forest using bootstrap sampling
• Predict(): Makes predictions using majority voting
• evaluateModel(): Calculates accuracy and confusion matrix
• crossValidation(): Performs k-fold cross-validation

Algorithm Details

1. Bootstrap Sampling: Each tree is trained on a random sample with replacement
2. Feature Subset Selection: Each split considers √(n_features) random features
3. Gini Impurity: Used as the splitting criterion
4. Majority Voting: Final predictions based on tree consensus

Configuration

The Random Forest can be configured with these parameters:

rf := NewRandomForest(
    100,  // n_estimators: Number of trees
    10,   // max_depth: Maximum tree depth
    5,    // min_samples_split: Minimum samples to split
    2,    // min_samples_leaf: Minimum samples in leaf
)

Performance Notes

• Training Speed: Much faster than Python/scikit-learn for medium datasets
• Memory Usage: Lower memory footprint than Python equivalent
• Scalability: Can handle larger datasets with same hardware
• Prediction Speed: Very fast inference due to compiled code

Extending the Implementation

To make this more feature-complete, you could add:

1. Better Data Preprocessing:

   // Add proper missing value handling
   // Implement label encoders for categorical variables
   // Add feature scaling/normalization

2. Model Persistence:

   // Serialize trained models to JSON/binary format
   // Load pre-trained models for inference

3. Parallel Training:

   // Use goroutines to train trees in parallel
   // Implement concurrent prediction

4. Advanced Metrics:

   // Add precision, recall, F1-score
   // Implement ROC curve analysis

5. Hyperparameter Tuning:

   // Grid search for optimal parameters
   // Random search implementation

Comparison with Python Version

Feature	Python (scikit-learn)	Go (Custom)
Training Speed	Moderate	Fast
Memory Usage	High	Low
Dependencies	Many (pandas, sklearn, matplotlib)	None
Code Complexity	Simple (library calls)	More complex (custom implementation)
Deployment	Requires Python environment	Single binary
Customization	Limited	Full control

Running Tests

You can test the implementation with sample data:

# Create sample CSV files with appropriate structure
# Run the program
go run main.go

# Expected output includes:
# - Dataset loading information
# - Training progress
# - Feature importance rankings
# - Model accuracy metrics
# - Cross-validation scores

This Go implementation provides a solid foundation for loan prediction modeling while demonstrating the performance benefits of compiled languages for machine learning tasks.