In machine learning, one of the primary goals is to build models that can accurately predict unseen data that is not part of the training set. However, building accurate models is a challenging task because a model that is too simple may not be able to capture the complexity of the data, and a model that is too complex may fit noise in the data instead of the underlying patterns. Therefore, it is crucial to find an optimal balance between model complexity and accuracy.
Overfitting and underfitting are two common problems that arise when training machine learning models. Overfitting occurs when a model fits the training data too well, including the noise, resulting in poor performance on unseen data. On the other hand, underfitting occurs when the model is too simple and does not capture the underlying patterns of the data, again leading to poor performance on unseen data.
Overfitting can occur when the model is too complex relative to the size of the training data or when we train the model for too many iterations. Overfitting can be detected by observing high training accuracy, but low test accuracy.
There are several ways to address overfitting, including:
1. Increasing the amount of training data
2. Using regularization techniques like L1 or L2 regularization to constrain the model’s parameters
3. Using dropout, which randomly drops out certain neurons during training to prevent co-adaptation of features
Underfitting can occur when we build a model that is not complex enough to capture the underlying patterns in the data. Underfitting can be detected by observing low training accuracy and low test accuracy.
There are several ways to address underfitting, including:
1. Increasing the complexity of the model
2. Using deeper or wider neural networks
3. Adding more features to the input data.
We can summarize the concept of overfitting and underfitting as optimizing a model to find a balance between bias and variance. A model with high bias has underfitting issue while a model with high variance has overfitting issue. We need to find a sweet spot between these two.