Q181: Any hypothesis find an approximation of the target function over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples. This is called _____.

(A) Hypothesis

(B) Inductive hypothesis

(C) Learning

(D) Concept learning

Q182: Principal component is a technique for

(A) Feature selection

(B) Dimensionality reduction

(C) Exploration

(D) None of the above

Q183: Data can broadly divided into following two types

(A) Qualitative

(B) Speculative

(C) Quantitative

(D) None of the above

Q184: Out of 200 emails, a classification model correctly predicted 150 spam emails and 30 ham emails. What is the accuracy of the model?

(A) 10%

(B) 90%

(C) 80%

(D) none of the above

Q185: In feature extraction, some of the commonly used ___ are used for combining the original features.

(A) Operators

(B) Delimiters

(C) Words

(D) All of the above

Q186: This type of interpretation of probability tries to quantify the uncertainty of some event and thus focuses on information rather than repeated trials.

(A) Frequency interpretation of probability

(B) Gaussian interpretation of probability

(C) Machine learning interpretation of probability

(D) Bayesian interpretation of probability

Q187: The probability that a particular hypothesis holds for a data set based on the Prior is called

(A) Independent probabilities

(B) Posterior probabilities

(C) Interior probabilities

(D) Dependent probabilities

Q188: Which is a type of machine learning where a target feature, which is of categorical type, is predicted for the test data on the basis of the information imparted by the training data?

(A) Unsupervised Learning

(B) Supervised Regression

(C) Supervised Classification

(D) Categorical Attribute

Q189: ———- are the data points (representing classes), the important component in a data set, which are near the identified set of lines (hyperplane).

(A) Hard Margin

(B) Soft Margin

(C) Linear Margin

(D) Support Vectors

Q190: In SVM, these functions take a lower dimensional input space and transform it to a higher dimensional space.