Example Concept: To improve a classification model's accuracy without overfitting, you can use techniques such as cross-validation, regularization, and feature selection. Cross-validation helps in assessing the model's performance across different subsets of the data, ensuring that it generalizes well. Regularization techniques like L1 (Lasso) and L2 (Ridge) add a penalty for larger coefficients, which discourages overly complex models. Feature selection involves choosing only the most relevant features, reducing noise and complexity. Additionally, techniques like dropout in neural networks or pruning in decision trees can help prevent overfitting by simplifying the model.