The insights from this work on overparameterized linear regression can be applied to improve the interpretability and robustness of other machine learning models, such as neural networks, when dealing with high-dimensional datasets. By understanding the concept of overparameterization and the implications of using a large number of predictors compared to the sample size, researchers can apply regularization techniques to neural networks to prevent overfitting and improve generalization. Additionally, the idea of removing noisy or improper predictors from the model can be translated to neural networks by implementing feature selection methods or dropout techniques to enhance model interpretability and reduce the impact of irrelevant or noisy input features. Overall, the principles of regularization, feature selection, and model simplification learned from overparameterized linear regression can be valuable in optimizing and refining the performance of neural networks on high-dimensional datasets.