can be used for regression as well as classification

optimize a convex cost function

always output values between 0 and 1

can be used for regression as well as classification

Learn Machine Learning MCQ Questions with answers and solutions.

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Q. Neural networks

(A) optimize a convex cost function
(B) always output values between 0 and 1
(C) can be used for regression as well as classification
(D) all of the above

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Q. Naive Bayes classifiers is . . . . . . . . Learning

(A) Supervised
(B) Unsupervised
(C) Both
(D) None

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Q. The distance between two points calculated using Pythagoras theorem is

(A) supremum distance
(B) eucledian distance
(C) linear distance
(D) manhattan distance

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Q. Features being classified is independent of each other in Nave Bayes Classifier

(A) FALSE
(B) a set of data is used to discover the potentially predictive relationship.
(C) ---
(D) ---

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Q. What does learning exactly mean?

(A) robots are programed so that they can perform the task based on data they gather from sensors.
(B) the svm allows high amount of error in classification
(C) learning is the ability to change according to external stimuli and remembering most of all previous experiences.
(D) it is a set of data is used to discover the potentially predictive relationship.

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Q. What do you mean by a hard margin?

(A) the svm allows very low error in classification
(B) improve the optimization algorithm being used for error minimization.
(C) both 1 & 2
(D) none of the above

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Q. Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?

(A) increase the amount of traning data in each traning set
(B) unsupervised learning
(C) decrease the model complexity
(D) reduce the noise in the training data

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Q. The problem of finding hidden structure in unlabeled data is called . . . . . . . .

(A) supervised learning
(B) speech recognition, regression
(C) reinforcement learning
(D) none of the above

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Q. Suppose you are training a linear regression model. Now consider these points.
1. Overfitting is more likely if we have less data
2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

(A) both are false
(B) 1 is false and 2 is true
(C) 1 is true and 2 is false
(D) both are true

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Q. Gini index does not favour equal sized partitions.

(A) TRUE
(B) Find interesting directions in data and find novel observations/ database cleaning
(C) ---
(D) ---

Machine Learning MCQs with answers Page - 2

Q. Neural networks

Q. Naive Bayes classifiers is . . . . . . . . Learning

Q. The distance between two points calculated using Pythagoras theorem is

Q. Features being classified is independent of each other in Nave Bayes Classifier

Q. What does learning exactly mean?

Q. What do you mean by a hard margin?

Q. Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?

Q. The problem of finding hidden structure in unlabeled data is called . . . . . . . .

Q. Suppose you are training a linear regression model. Now consider these points.
1. Overfitting is more likely if we have less data
2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

Q. Gini index does not favour equal sized partitions.

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Machine Learning MCQs with answers Page - 2

Q. Neural networks

Q. Naive Bayes classifiers is . . . . . . . . Learning

Q. The distance between two points calculated using Pythagoras theorem is

Q. Features being classified is independent of each other in Nave Bayes Classifier

Q. What does learning exactly mean?

Q. What do you mean by a hard margin?

Q. Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?

Q. The problem of finding hidden structure in unlabeled data is called . . . . . . . .

Q. Suppose you are training a linear regression model. Now consider these points. 1. Overfitting is more likely if we have less data 2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

Q. Gini index does not favour equal sized partitions.

Add MCQ in this Category

Browse Categories

Q. Suppose you are training a linear regression model. Now consider these points.
1. Overfitting is more likely if we have less data
2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?