- Overfitting and Underfitting are responsible for poor performance.
- Overfitting gives a good performance on the trained data, poor generalization to other data.
- Underfitting gives poor performance on the training data and good generalization to other data
In the context of machine learning, overfitting and underfitting are two common issues that occur when training a model:
- Overfitting: Overfitting occurs when a model learns to perform well on the training data but fails to generalize to unseen data. In other words, the model captures noise or random fluctuations in the training data as if they were meaningful patterns. This often happens when the model is too complex relative to the amount and quality of the training data. Signs of overfitting include excessively high accuracy on the training data but poor performance on validation or test data.
- Underfitting: Underfitting, on the other hand, occurs when a model is too simple to capture the underlying structure of the data. In this case, the model fails to learn the relationships present in the training data and performs poorly not only on the training data but also on unseen data. Signs of underfitting include low accuracy on both the training and validation/test data.
To address these issues:
- Overfitting: Techniques to combat overfitting include using simpler models, reducing the complexity of the current model (e.g., by reducing the number of parameters or adding regularization techniques such as L1 or L2 regularization), increasing the amount of training data, or using techniques like dropout or data augmentation.
- Underfitting: To mitigate underfitting, you might need to use a more complex model, increase the number of features used in the model, or gather more relevant training data.
The goal in machine learning is to strike a balance between overfitting and underfitting, achieving a model that generalizes well to unseen data without sacrificing performance on the training data. Regularization techniques, cross-validation, and careful hyperparameter tuning are often employed to achieve this balance.