Overfitting and underfitting are two common problems in machine learning that can negatively impact the performance of a model on new data. Here are some key points from the search results:
Underfitting
- Occurs when a model is too simple for the data and cannot perform well on either the training or new data.
- Reasons for underfitting include high bias, low variance, a model that is too simple, and training data that is not cleaned or contains noise.
- Techniques to reduce underfitting include increasing model complexity, increasing the number of features, performing feature engineering, and removing noise from the data.
Overfitting
- Occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
- Reasons for overfitting include low bias, high variance, a model that is too complex, and training data that is not cleaned or contains noise.
- Techniques to reduce overfitting include using K-fold cross-validation, using regularization, reducing model complexity, and increasing the size of the training dataset.
In summary, underfitting occurs when a model is too simple for the data, while overfitting occurs when a model is too complex and learns the noise in the training data. Both problems can be addressed by adjusting the model complexity, increasing the size of the training dataset, and cleaning the data.
