Underfitting and Overfitting:

Underfitting and overfitting are two common problems that can occur in machine learning. Both problems happen when a model is not able to generalize well to new data, but they are caused by different things.

Underfitting occurs when a model is too simple and cannot learn the underlying patterns in the training data. This can be caused by using a model with too few parameters, not enough training data, or features that are not representative of the underlying problem. An underfitted model will perform poorly on both the training and test data.

 

Overfitting occurs when a model learns the training data too well, including the noise in the data. This can be caused by using a model with too many parameters, too much training data, or features that are not relevant to the underlying problem. An overfitted model will perform well on the training data but poorly on the test data.

 

How to avoid underfitting:

  • Use a model that is appropriate for the complexity of the data. A more complex model can learn more complex patterns in the data, but it is also more likely to overfit.
  • Use a dataset that is large enough and representative of the real-world data that the model will be used on. A small or unrepresentative dataset can lead to underfitting.
  • Use regularization techniques to prevent the model from overfitting. Regularization techniques add a penalty to the model for being too complex.

How to avoid overfitting:

  • Use a simpler model. A simpler model is less likely to overfit, but it may be less accurate.
  • Use less training data. A smaller training dataset can help to prevent the model from learning the noise in the data.
  • Use feature selection to remove irrelevant features. Irrelevant features can lead to overfitting.
  • Use regularization techniques. Regularization techniques add a penalty to the model for being too complex.

Examples of underfitting and overfitting:

  • Underfitting: A model for predicting house prices might not be able to take into account factors such as the size of the house, the location of the house, and the condition of the house. This could be because the model is too simple, or because the training data does not include all of these factors.
  • Overfitting: A model for predicting house prices might learn the noise in the training data, such as the names of the sellers or the dates on which the houses were sold. This could be because the model is too complex, or because the training data is too small.