In this lesson, we will deepen our understanding about two common challenges faced while training machine learning models: overfitting and underfitting.

First, let's define these terms. Overfitting happens when a model learns the training data so well that it even catches the irrelevant details or noise in the data. Thus, it performs well on the training data but fails on unseen data or test data because it's unable to generalize the patterns for new, real-world data.

On the contrary, underfitting happens when a model performs poorly on both the training and test data because it cannot capture the underlying pattern of the data. This situation is mostly due to the simplicity of the model, and it too fails to generalize on the unseen data. In terms of error rates, overfitting gives a low training error but a high test error, while underfitting gives a high error for both datasets.

Finding the balance between overfitting and underfitting while training models is crucial. Here are some techniques to avoid overfitting or underfitting:

• Regularization: Regularization techniques add a penalty term to the error of the model to limit the permissible complexity of the model. They are effective for overfitting prevention by making the model simpler and more general.

• Adding More Data: A larger training dataset may help decrease overfitting, because the more data we have, the better our model can learn from it and generalize upon unseen data.

• Early Stopping: Early stopping is a technique to avoid overfitting by halting the training once the model's performance begins to degrade on a held-out validation set.

• Cross-Validation: Cross-Validation splits the dataset into multiple parts, training on some and validating on others to assess model consistency and detect overfitting or underfitting by evaluating performance across varied data subsets.

The key is to reach a fair trade-off between bias (underfitting) and variance (overfitting) such that your model works well on unseen data.