Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Why Feature Selection

The video discusses feature selection, a dimensionality reduction technique that improves model performance by reducing complexity and enhancing generalization. It compares feature selection with feature extraction, highlighting the ability to retain original feature identities for better model interpretation. The video also emphasizes the cost-effectiveness of feature selection in data acquisition by focusing on relevant features.

Why is feature selection considered a dimensionality reduction technique?

• It increases the number of features.

• It reduces the number of features.

• It adds more complexity to the model.

• It has no effect on dimensionality.

What is the relationship between model complexity and the number of parameters?

• The number of parameters has no impact on complexity.

• More parameters can increase the risk of overfitting.

• Fewer parameters mean a more complex model.

• More parameters always lead to better performance.

How does selecting a subset of features affect model generalization?

• It decreases generalization capacity.

• It increases the risk of overfitting.

• It improves generalization by reducing complexity.

• It has no effect on generalization.

What is a key difference between feature selection and feature extraction?

• Feature extraction is always preferred over feature selection.

• Feature selection changes the original features.

• Feature selection maintains original feature identities.

• Feature extraction keeps the original feature identities.

Why might feature selection be preferred for model interpretability?

• It reduces the model's generalization capacity.

• It obscures the relationship between features and labels.

• It allows for better understanding of feature importance.

• It makes the model more complex.

How can feature selection improve the data acquisition process?

• By increasing the number of irrelevant features.

• By focusing on acquiring only important features.

• By ignoring the importance of features.

• By making data acquisition more expensive.

What is a common benefit of both feature selection and feature extraction?

• They both obscure feature identities.

• They both improve model generalization.

• They both reduce model performance.

• They both increase model complexity.