Skip to main content Skip to footer
  • Home
  • Interview Questions
    • Machine Learning Basics
    • Deep Learning
    • Supervised Learning
    • Unsupervised Learning
    • Natural Language Processing
    • Statistics
    • Data Preparation
  • Technical Quizzes
  • Jobs
  • Home
  • Interview Questions
    • Machine Learning Basics
    • Deep Learning
    • Supervised Learning
    • Unsupervised Learning
    • Natural Language Processing
    • Statistics
    • Data Preparation
  • Technical Quizzes
  • Jobs
LoginSign Up
Explore Questions by Topics
  • Computer Vision (1)
  • Generative AI (2)
  • Machine Learning Basics (18)
  • Deep Learning (52)
    • DL Basics (16)
    • DL Architectures (17)
      • Feedforward Network / MLP (2)
      • Sequence models (6)
      • Transformers (9)
    • DL Training and Optimization (17)
  • Natural Language Processing (27)
    • NLP Data Preparation (18)
  • Supervised Learning (115)
    • Regression (41)
      • Linear Regression (26)
      • Generalized Linear Models (9)
      • Regularization (6)
    • Classification (70)
      • Logistic Regression (10)
      • Support Vector Machine (9)
      • Ensemble Learning (24)
      • Other Classification Models (9)
      • Classification Evaluations (9)
  • Unsupervised Learning (55)
    • Clustering (37)
      • Distance Measures (9)
      • K-Means Clustering (9)
      • Hierarchical Clustering (3)
      • Gaussian Mixture Models (5)
      • Clustering Evaluations (6)
    • Dimensionality Reduction (9)
  • Statistics (34)
  • Data Preparation (35)
    • Feature Engineering (30)
    • Sampling Techniques (5)

Regularization

  • Q. What is Regularization?
  • Q. What does L1 regularization (Lasso) mean?
  • Q. What does L2 regularization (Ridge) mean?
  • Q. When to use Ridge Regression vs Lasso?
  • Q. How would you perform feature selection using Lasso?
  • Q. What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Partner Ad
Explore Questions by Topics
  • Computer Vision (1)
  • Generative AI (2)
  • Machine Learning Basics (18)
  • Deep Learning (52)
    • DL Basics (16)
    • DL Architectures (17)
      • Feedforward Network / MLP (2)
      • Sequence models (6)
      • Transformers (9)
    • DL Training and Optimization (17)
  • Natural Language Processing (27)
    • NLP Data Preparation (18)
  • Supervised Learning (115)
    • Regression (41)
      • Linear Regression (26)
      • Generalized Linear Models (9)
      • Regularization (6)
    • Classification (70)
      • Logistic Regression (10)
      • Support Vector Machine (9)
      • Ensemble Learning (24)
      • Other Classification Models (9)
      • Classification Evaluations (9)
  • Unsupervised Learning (55)
    • Clustering (37)
      • Distance Measures (9)
      • K-Means Clustering (9)
      • Hierarchical Clustering (3)
      • Gaussian Mixture Models (5)
      • Clustering Evaluations (6)
    • Dimensionality Reduction (9)
  • Statistics (34)
  • Data Preparation (35)
    • Feature Engineering (30)
    • Sampling Techniques (5)
Join us on:
  • Machine Learning Interview Preparation Group
  • @OfficialAIML
Find out all the ways that you can
Contribute
Other Questions in Regularization
  • Explain the concept of Linear Regression
  • How are coefficients of linear regression estimated?
  • What are the assumptions of linear regression? 
  • How is variability measured in Linear Regression?
  • What are the evaluation criteria for a Linear Regression model? 
  • How can categorical predictors be incorporated in linear regression?
© 2025 AIML.COM  |  ♥ Sunnyvale, California