Machine Learning MCQ

Below Machine Learning quiz are Multiple Choice Questions (MCQs) type Quiz. These Machine Learning MCQ Questions helps you to refresh your Machine Learning, you can see the correct option by clicking on it. .
  • 1. Because of low bias and high variance , we get _____ model

    • high error
    • perfectly fitting
    • underfitting
    • over fitting
  • 2. ML is a field of AI consisting of learning algorithms that?

    • Improve their performance
    • At executing some task
    • Over time with experience
    • All of the above
  • 3. Targeted marketing, Recommended Systems, and Customer Segmentation are applications in

    • Unsupervised Learning: Clustering
    • Supervised Learning: Classification
    • Reinforcement Learning
    • Unsupervised Learning: Regression
  • 4. You are given reviews of movies marked as positive, negative, and neutral. Classifying reviews of a new movie is an example of

    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
    • None of these
  • 5. Which of the following is not type of learning?

    • Semi-unsupervised Learning
    • Unsupervised Learning
    • Supervised Learning
    • Reinforcement Learning
  • 6. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Which of the following statement is true in following case?

    • Feature F1 is an example of nominal variable.
    • Feature F1 is an example of ordinal variable.
    • It doesn’t belong to any of the above category.
    • Both (a) and (b)
  • 7. Which of the following is an example of a deterministic algorithm?

    • K-Means
    • PCA
    • Both of these
    • None of these
  • 8. What kind of distance metric(s) are suitable for categorical variables to finding the closest neighbors

    • Euclidean Distance
    • Manhattan distance
    • Minkowski distance
    • Hamming distance
  • 9. Several sets of data related to each other used to make decisions in machine learning algorithms

    • unsupervised learning
    • Classifiers
    • supervised learning
    • Dataset
  • 10. Which feature selection technique uses shrinkage estimators to remove redundant features from data?

    • Stepwise regression
    • Sequential feature selection
    • Neighborhood component selection
    • Regularization
  • 11. When would you reduce dimensions in your data?

    • When data comes from sensor
    • When you are using a Linux machine
    • When your data set is larger than 500GB
    • When you have larger set of features with similar characteristics
  • 12. ______ is a classification algorithm used to assign observations to a discrete set of classes.

    • Linear Regression
    • Multiple Linear Regression
    • Logistic Regression
    • Classification
  • 13. What kind of learning algorithm for "Facial identities or facial expressions"?

    • Recognizing Anomalies
    • Prediction
    • Generating Patterns
    • Recognition Patterns
  • 14. Which one in the following is not Machine Learning disciplines?

    • Information Theory
    • Neurostatistics
    • Optimization + Control
    • Physics
  • 15. Targetted marketing, Recommended Systems, and Customer Segmentation are applications in ...

    • Unsupervised Learning: Clustering
    • Supervised Learning: Classification
    • Reinforcement Learning
    • Unsupervised Learning: Regression
  • 16. What kind of learning algorithm for "Future stock prices or currency exchange rates"?

    • Prediction
    • Recognizing Anomalies
    • Generating Patterns
    • Recognition Patterns
  • 17. Machine Learning has various search/ optimization algorithms, which of the following is not evolutionary computation?

    • Perceptron
    • Genetic Algorithm (GA)
    • Neuro Evolution
    • Genetic Programming (GP)
  • 18. KNN is ___________ algorithm

    • Non-parametric and Lazy Learning
    • Parametric and Lazy Learning
    • Parametric and Eager Learning
    • Non-parametric and Eager Learning
  • 19. How many types of machine learning?

    • 4
    • 3
    • 2
    • 1