Overfitting and underfitting are two common problems that can occur when training machine learning models.
Overfitting:
Overfitting occurs when a model is too complex and learns the noise and random fluctuations in the training data, rather than the underlying patterns. As a result, the model performs well on the training data but poorly on new, unseen data.
Example:
Suppose we’re trying to predict the price of a house based on its size and number of bedrooms. We collect a dataset of 100 houses and train a model that includes many intricate features, such as the number of windows, doors, and even the color of the walls. The model fits the training data perfectly, but when we test it on a new set of 100 houses, it performs poorly.
This is because the model has overfitted to the training data and has learned the specific characteristics of each house in the training set, rather than the general patterns that apply to all houses.
Underfitting:
Underfitting occurs when a model is too simple and fails to capture the underlying patterns in the data.
Example:
Suppose we’re trying to predict the price of a house based on its size and number of bedrooms, but we only use a simple linear model that considers only the size of the house. The model performs poorly on both the training data and new data, because it has oversimplified the relationship between the features and the target variable.
This is because the model has underfitted the data and has failed to capture the additional information provided by the number of bedrooms.
Solution:
To avoid overfitting and underfitting, we need to find a balance between model complexity and data complexity. This can be achieved by:
• Using regularization techniques to reduce model complexity
• Using cross-validation to evaluate model performance on unseen data
• Using techniques like early stopping to prevent overfitting
• Using ensemble methods to combine multiple models and reduce overfitting