Describe the concept of overfitting and underfitting in ML and the strategies to mitigate them.

In machine learning (ML), overfitting and underfitting are two common problems that occur when training a model. Both issues impact the model's ability to generalize well to unseen data. Let's delve into the concepts of overfitting and underfitting, as well as strategies to mitigate them: 1. Overfitting: Overfitting occurs when a model learns to perform exceptionally well on the training data but fails to generalize to new, unseen data. In other words, the model becomes too complex and learns the noise or specific patterns in the training set, instead of capturing the underlying relationships in the data. Signs of overfitting include excessively low training error but high testing error. Some key causes of overfitting are: * Insufficient data: When the training dataset is small, the model may memorize the limited examples instead of learning meaningful patterns. * Model complexity: A highly complex model with a large number of parameters can capture noise and intricacies in the training data, leading to overfitting. * Lack of regularization: Insufficient regularization techniques, such as L1 or L2 regularization, dropout, or early stopping, can exa....