Machine Learning MCQ Questions: Practice and Test Your Knowledge

We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?

If two variables are correlated, is it necessary that they have a linear relationship?

Suppose we fit "Lasso Regression" to a data set, which has 100 features (X1,X2...X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter. Now, which of the following option will be correct?

Which of the following is not supervised learning?

Some telecommunication company wants to segment their customers into distinct groups ,this is an example of

. . . . . . . . which can accept a NumPy RandomState generator or an integer seed.

The kernel trick

Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node

The number of iterations in apriori . . . . . . . . Select one:

Skewness of Normal distribution is . . . . . . . .