nerdexam
Google

PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #83

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from

The correct answer is B. Use L1 regularization to reduce the coefficients of uninformative features to 0.. L1 regularization it's good for feature selection https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-

Submitted by omar99· Apr 18, 2026ML model development

Question

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

Options

  • AUse principal component analysis (PCA) to eliminate the least informative features.
  • BUse L1 regularization to reduce the coefficients of uninformative features to 0.
  • CAfter building your model, use Shapley values to determine which features are the most
  • DUse an iterative dropout technique to identify which features do not degrade the model when

How the community answered

(26 responses)
  • A
    4% (1)
  • B
    77% (20)
  • C
    15% (4)
  • D
    4% (1)

Explanation

L1 regularization it's good for feature selection https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-

Topics

#Feature Selection#L1 Regularization#Linear Models#Regularization

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice