Elaborate on techniques that can be applied to detect and mitigate overfitting and underfitting in a machine learning model.

Overfitting and underfitting are two common challenges in machine learning that impact a model's ability to generalize to new data. Overfitting occurs when a model learns the training data too well, capturing noise and random variations rather than the underlying patterns. This results in a model that performs excellently on the training data but poorly on unseen data. Underfitting, on the other hand, happens when a model fails to capture the underlying patterns in the training data, resulting in poor performance on both the training and the unseen data. It essentially is too simple for the underlying complexity of the data. Here are techniques that can be used to detect and mitigate both overfitting and underfitting: Detecting Overfitting: 1. Performance Discrepancy: The most common way to detect overfitting is to see the difference in performance between the training set and the validation set. If a model performs very well on the training data but poorly on the validation or test set, this is a strong indication of overfitting. For example, in a regression problem, if the model achieves very low RMSE on the training set but much higher RMSE on the test set, overfitting has likely occurred. Similarly, in a classification problem, a model might achieve an accuracy of 99% on the training set, but only 70% on the test set, showing the same pattern. 2. Learning Curves: Learning curves are plots that show how the training and validation performance metrics change as the training set size increases. With overfitting, the training performance remains high while the validation performance plateaus or even declines as the training size increases. The gap between the curves would also show the level of overfitting, with larger gaps indicating more overfitting. On the other hand, with underfitting the training and validation curves both plateau at a low performance level. 3. Model Complexity: Overfitted models are often overly complex, having too many parameters for the amount of data available, this usually leads to overfitting. For instance, a deep neural network with many layers may overfit on a small dataset due to its high complexity, whereas a simple linear regression might underfit on the same data set, because it is too simple. 4. High Variance: Overfitting is also associated with high variance, meaning the model is highly sensitive to small changes in the training data. This can be assessed using cross-validation where the scores between folds will have high variance. If performance....