Question

Question 14 (1 point) Saved [Right minus wrong] Both PCA and Lasso can be used for feature selection. Which of the following statements are true? Lasso selects a subset (not necessarily a strict subset) of the original features PCA and Lasso both allow you to specify how many features are chosen PCA produces features that are linear combinations of the original features

Answer 1

Both PCA and Lasso can be used for feature selection. Which of the following statements are true ?

1. Lasso selects a subset (not necessarily a strict subset) of the original features - True

Lasso is used in machine learning for regression analysis method where variable selection is used to improve prediction accuracy. What Lasso does is that it puts a constraint on the sum of the values available and if it doesnt pass the constraint, they are shrunk to zero so technically Lasso selects only the subset of numbers available to it.

2. PCA and Lasso both allow you to specify how many features are chosen - False

They necessarily don't allow you to specify it and choses according to the constraints or other factors set.

3. PCA produces features that are linear combination of the original features - True

PCA is a statistical analysis where the values available to them are converted into linearly non corelated values called principal components which makes the above statement True