## Machine Learning Set 2

Free Online Best Machine Learning MCQ Questions for improve your basic knowledge of Machine Learning. This Machine Learning Set 2 test that contains 25 Multiple Choice Questions with 4 options. You have to select the right answer to a question.

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Question 1 |

How we split data in Machine Learning?

A | Training Data |

B | Validation Data |

C | Testing Data |

D | All of the Above |

Question 2 |

Define INFORMATION?

A | Data that has been interpreted and manipulated and has now some meaningful inference for the users |

B | DATA can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed |

C | Combination of inferred information, experiences, learning and insights. Results in awareness or concept building for an individual or organization |

D | All of the Above |

Question 3 |

Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)

A | Models which underfit have a low variance |

B | Models which overfit have a high bias. |

C | Models which overfit have a low bias. |

D | Models which underfit have a high variance. |

Question 4 |

What is meant by Test Set?

A | Testing set is the portion of the dataset used to train the model. |

B | Testing set is the portion of the dataset used to test the trained model |

C | Testing set is part of data which is used to do a frequent evaluation of model, fit on training dataset along with improving involved hyperparameters |

D | both a and b |

Question 5 |

Properties of Data_____

A | Volume,Variety |

B | Variety,Veracity |

C | value |

D | All of the Above |

Question 6 |

What is meant by Validation Data?

A | Validation Data is part of data which is used to do a frequent evaluation of model, fit on training dataset along with improving involved hyperparameters |

B | Validation Data is the portion of the dataset used to train the model. |

C | Validation Data is the portion of the dataset used to test the trained model |

D | both a and b |

Question 7 |

Which of the following learning algorithms will return a classifier if the training data is not linearly separable?

A | Hard margin SVM |

B | Soft margin SVM |

C | Perceptron |

D | d) Naïve bayes |

Question 8 |

Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. What kind of learning problem is this?

A | Supervised learning |

B | Unsupervised learning |

C | Both (a) and (b) |

D | Neither (a) nor (b) |

Question 9 |

Suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation?

A | P(E, F), P(H), P(E|H), P(F|H) |

B | P(E, F), P(H), P(E, F|H) |

C | P(H), P(E|H), P(F|H) |

D | P(E, F), P(E|H), P(F|H) |

Question 10 |

High entropy means that the partitions in classification are

A | pure |

B | not pure |

C | useful |

D | useless |

Question 11 |

Which of the following best describes the joint probability distribution P(X, Y, Z) for the given Bayes net.

A | P(X, Y, Z) = P(Y) * P(X|Y) * P(Z|Y) |

B | P(X, Y, Z) = P(X) * P(Y|X) * P(Z|Y) |

C | P(X, Y, Z) = P(Z) * P(X|Z) * P(Y|Z) |

D | P(X, Y, Z) = P(X) * P(Y) * P(Z) |

Question 12 |

A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

A | 12 |

B | 24 |

C | 48 |

D | 72 |

Question 13 |

As the number of training examples goes to infinity, your model trained on that data will have:

A | Lower variance |

B | Higher variance |

C | Same variance |

D | None of the above |

Question 14 |

The numerical output of a sigmoid node in a neural network:

A | Is unbounded, encompassing all real numbers. |

B | Is unbounded, encompassing all integers. |

C | Is bounded between 0 and 1. |

D | Is bounded between -1 and 1. |

Question 15 |

We split the given data set into____different sections

A | two |

B | three |

C | four |

D | five |

Question 16 |

Define KNOWLEDGE?

A | Combination of inferred information, experiences, learning and insights. Results in awareness or concept building for an individual or organization |

B | Data that has been interpreted and manipulated and has now some meaningful inference for the users |

C | DATA can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed |

D | All of the Above |

Question 17 |

Which of the following best describes what discriminative approaches try to model? (w are the parameters in the model)

A | p(y|x, w) |

B | p(y, x) |

C | p(w|x, w) |

D | None of the above |

Question 18 |

Which of the following methods can achieve zero training error on any linearly separable dataset?

A | Decision tree |

B | Perceptron |

C | 15-nearest neighbors |

D | Logistic regression |

Question 19 |

What would you do in PCA to get the same projection as SVD?

A | Transform data to zero mean |

B | Transform data to zero median |

C | Not possible |

D | None of these |

Question 20 |

The number of test examples needed to get statistically significant results should be________

A | Larger if the error rate is larger. |

B | Larger if the error rate is smaller. |

C | Smaller if the error rate is smaller. |

D | It does not matter. |

Question 21 |

Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is______

A | higher |

B | same |

C | lower |

D | it could be any of the above |

Question 22 |

What is meant by Training set?

A | Training set is the portion of the dataset used to test the trained model |

B | Training set is the portion of the dataset used to train the model. |

C | Training set is part of data which is used to do a frequent evaluation of model, fit on training dataset along with improving involved hyperparameters |

D | both a and b |

Question 23 |

Predicting on whether will it rain or not tomorrow evening at a particular time is a type of _________ problem.

A | Classification |

B | Regression |

C | Unsupervised learning |

D | All of the above |

Question 24 |

In terms of the bias-variance trade-off, which of the following is substantially more harmful to the test error than the training error?

A | Bias |

B | Loss |

C | Variance |

D | Risk |

Question 25 |

Which of the following is/are true regarding an SVM?

A | For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line. |

B | In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane. |

C | For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion. |

D | Overfitting in an SVM is not a function of number of support vectors. |

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