Explain the implications of high bias and high variance in a machine learning model, and describe three techniques to mitigate each problem.

High bias and high variance are two common problems that can significantly impact the performance of a machine learning model. They represent two extremes of model complexity, and understanding their implications is crucial for building effective models. High bias occurs when a model is too simple to capture the underlying patterns in the data. Such a model makes strong assumptions about the data, leading to underfitting. In essence, the model is not flexible enough to learn the true relationship between the features and the target variable. A highly biased model will typically have low accuracy on both the training and test datasets because it misses important trends in the data. An example of a highly biased model would be fitting a linear regression to data that is clearly non-linear. The linear model, due to its inherent simplicity, will consistently fail to accurately predict the target variable. High variance, on the other hand, occurs when a model is too complex and learns the noise in the training data rather than the underlying signal. This leads to overfitting, where the model performs well on the training data but poorly on unseen data (the test dataset). A highly variable model is overly sensitive to the specific details of the training set and does not generalize well to new data. An example would be fitting a very high-degree polynomial to a dataset; the model will perfectly fit the training data, capturing even the random fluctuations, but will perform poorly on new data due to its extreme sensitivity to the training data's peculiarities. Here are three techniques to mitigate....