In machine learning, finding the right balance between simplicity and complexity is an art and a challenge in creating a model that performs well on unseen cases. Overfitting in machine learning occurs when the model performs well during the training process but performs poorly on unseen data. It happens when the model is too complex and captures noise or random fluctuations in the training data. As you might have guessed, underfitting is the opposite and occurs when the model is too simple to capture the underlying patterns.
In the pursuit of finding the perfect model, we follow these steps. To prevent overfitting, we can use regularization techniques to impose constraints on the model, discouraging it from fitting noise in the training data. L1 and L2 regularization and dropout are some techniques to prevent the model from overfitting.
On the other side, to address underfitting issues, we can select a more complex model with more parameters, feed it better features, and reduce constraints on the model. Cross-validation is also a valuable tool to assess a model's generalization performance. By dividing the dataset into multiple subsets and training the model on different combinations of these subsets, this process helps identify if a model is overfitting or underfitting and guides adjustments to strike the optimal balance.
