Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?
a. we can still classify data correctly for given setting of hyper parameter c
b. we can not classify data correctly for given setting of hyper parameter c
c. cant say
d. none of these
Q. Which of the following is true about Naive Bayes ?
a. assumes that all the features in a dataset are equally important
b. b. assumes that all the features in a dataset are independent
c. both a and b
d. none of the above option
Q. can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.
a. supervised
b. semi-supervised
c. reinforcement
d. clusters
Q. In reinforcement learning, this feedback is
a. overfitting
b. overlearning
c. reward
d. none of above
Q. In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called .
a. deep learning
b. machine learning
c. reinforcement learning
d. unsupervised learning
Q. showed better performance than other approaches, even without a context- based model
a. machine learning
b. deep learning
c. reinforcement learning
d. supervised learning