Machine Learning MCQ Questions: Practice and Test Your Knowledge

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 it's hyper parameter.What would happen when you use very small C (C~0)?

Which of the following is a reasonable way to select the number of principal components "k"?

If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class . . . . . . . .

Linear SVMs have no hyperparameters that need to be set by cross-validation

Impact of high variance on the training set ?

Which of the following are components of generalization Error?

Which statement is true about neural network and linear regression models?

What are support vectors?

Which of the following can act as possible termination conditions in K-Means?
1. For a fixed number of iterations.
2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.
3. Centroids do not change between successive iterations.
4. Terminate when RSS falls below a threshold.

Classification rules are extracted from . . . . . . . .