13. Cross Validation Demo
Cd13639 C1 L4 DEMO 4 V1
Enhancing Model Performance with Cross Validation
Objective: Introduce cross validation to improve decision trees through hyperparameter tuning.
Concepts Explained:
- Cross Validation: Divides dataset into subsections or "folds", ensuring broader model training and better generalization by avoiding overfitting.
- Hyperparameter Tuning: Uses GridSearchCV to optimize decision tree components.
Process Outline:
- Library Imports: Decision tree classifier and GridSearchCV for managing cross validation.
- Data Preparation:
- Involve existing datasets to calculate total returns
- Creation of indicator variables and split into training/testing sets (70% train, 30% test)
- Standardization: Ensure feature uniformity by standardizing training data.
- Model Training & Evaluation:
- Apply GridSearchCV to specify hyperparameter grids (e.g., gini impurity, max depth, minimum sample splits)
- Employ five-fold cross validation for comprehensive testing
- Evaluate performances (Benchmark vs. Decision Tree CV Model)
Outcome: Enhanced Decision Tree CV model resulted in improved returns (14.9% CAGR) with consistent performance across most tested periods. This demonstrates the effectiveness of cross validation in optimizing model reliability and accuracy.