Decision Trees in Machine Learning
To assess the accuracy and effectiveness of decision trees, several evaluation metrics are employed. These include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Cross-validation techniques like k-fold cross-validation and stratified sampling can help in obtaining more reliable performance estimates.
Improving Decision Trees
To improve the performance of decision trees, several strategies can be employed. Pruning, which involves removing unnecessary branches or nodes, helps prevent overfitting and improves generalization. Ensemble methods like random forests combine multiple decision trees to obtain more accurate and robust predictions. Feature selection techniques and hyperparameter tuning also contribute to enhancing the overall performance of decision trees.
