Practice Quiz on Overfitting in Machine Learning

Study Notes

Overfitting occurs when a model is too complex and learns the noise in the training data, leading to poor performance on new, unseen data.
To prevent overfitting, the training process should be stopped before the model starts capturing noise from the data, known as early stopping.
Increasing the training set by including more data can help prevent overfitting by providing more opportunities to discover relationships between input and output variables.

Feature selection involves identifying the most important features within training data and removing redundant or less important features.
Feature selection helps simplify the model, reduce noise, and prevent overfitting.

Cross-validation is a powerful technique to prevent overfitting by dividing the dataset into k-equal-sized subsets (folds) and training the model on each fold.

Early stopping: pausing the training process before the model starts learning noise.
Training with more data: increasing the training set to provide more opportunities to discover relationships between input and output variables.
Feature selection: identifying the most important features and removing redundant or less important ones.
Cross-validation: dividing the dataset into k-equal-sized subsets (folds) and training the model on each fold.
Data augmentation: increasing the size of the training set by applying transformations to existing data.
Regularization: adding a penalty term to the loss function to discourage large weights.

Underfitting occurs when a model is too simple and fails to capture patterns in the data, leading to poor performance on both training and new data.
Reasons for underfitting include:
- The model is too simple.
- The size of the training dataset is too small.
- The model has a high bias.