Underfitting and Overfitting Quiz

Model Complexity and Feature Selection

• Underfitting in machine learning occurs when a model is too simple and cannot capture the underlying patterns in the data.
• Solution to underfitting: Increase model complexity by adding more features or using a more complex model.

Overfitting and Its Solutions

• Overfitting in machine learning occurs when a model is too complex and learns the noise in the data rather than the underlying patterns.
• Solution to overfitting: Regularization techniques, early stopping, data augmentation, and ensemble methods to reduce model complexity.

Model Complexity and Feature Selection Goals

• The goal in machine learning with respect to model complexity is to find a balance between simplicity and complexity to avoid underfitting and overfitting.
• The primary focus of feature selection in machine learning is to select a subset of relevant features that best predict the target variable.

Variables, Features, and Selection

• In machine learning, a variable is a characteristic or attribute of the data, whereas a feature is a specific representation of a variable used in the model.
• Selection is considered stronger than ranking in variable selection because it provides a subset of the most relevant features, whereas ranking provides a list of features in order of importance.

Ultimate Goal and Biomedical Applications

• The ultimate goal in machine learning with respect to the relationship between f(x) and y is to learn a mapping between the input features and the target variable that generalizes well to new, unseen data.
• Variable selection is relevant for biomedical applications in machine learning because it helps identify the most informative biomarkers or genes that are associated with a particular disease or phenotype.