R4RIN – MCQs, Mock Tests | Machine Learning (ML) Machine Learning (ML) MCQ Set 04

What is gini index?

1. gini index??operates on the categorical target variables
2. it is a measure of purity
3. gini index performs only binary split
4. all (1,2 and 3)

✅ Correct Answer: 4

Tree/Rule based classification algorithms generate ... rule to perform the classification.

1. if-then.
2. while
3. do while
4. switch

✅ Correct Answer: 1

Decision Tree is

1. flow-chart
2. structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
3. both a & b
4. class of instance

✅ Correct Answer: 3

Which of the following is true about Manhattan distance?

1. it can be used for continuous variables
2. it can be used for categorical variables
3. it can be used for categorical as well as continuous
4. it can be used for constants

✅ Correct Answer: 1

A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?Note: Model has successfully deployed and no technical issues are found at client side except the model performance

1. it is probably a overfitted model
2. ??it is probably a underfitted model
3. ??can’t say
4. wrong client data

✅ Correct Answer: 1

Which statement is true about the K-Means algorithm? Select one:

1. the output attribute must be cateogrical.
2. all attribute values must be categorical.
3. all attributes must be numeric
4. attribute values may be either categorical or numeric

✅ Correct Answer: 3

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.

1. 1, 3 and 4
2. 1, 2 and 3
3. 1, 2 and 4
4. 1,2,3,4

✅ Correct Answer: 4

Which of the following statement is true about k-NN algorithm? 1) k-NN performs much better if all of the data have the same scale 2) k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large 3) k-NN makes no assumptions about the functional form of the problem being solved

1. 1 and 2
2. 1 and 3
3. only 1
4. 1,2 and 3

✅ Correct Answer: 4

In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes

1. 1 and 2
2. 2 and 3
3. 1, 2, and 3??
4. 1 and 3

✅ Correct Answer: 3

his clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:

1. k-means clustering
2. conceptual clustering
3. expectation maximization
4. agglomerative clustering

✅ Correct Answer: 1

Which one of the following is the main reason for pruning a Decision Tree?

1. to save computing time during testing
2. to save space for storing the decision tree
3. to make the training set error smaller
4. to avoid overfitting the training set

✅ Correct Answer: 4

You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?

1. your decision trees are too shallow.
2. you need to increase the learning rate.
3. you are overfitting.
4. incorrect data

✅ Correct Answer: 1

The K-means algorithm:

1. requires the dimension of the feature space to be no bigger than the number of samples
2. has the smallest value of the objective function when k = 1
3. minimizes the within class variance for a given number of clusters
4. converges to the global optimum if and only if the initial means are chosen as some of the samples themselves

✅ Correct Answer: 3

Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2. Complete-link 3. Average-link

1. 1 and 2
2. 1 and 3
3. 2 and 3
4. 1, 2 and 3

✅ Correct Answer: 4

In which of the following cases will K-Means clustering fail to give good results?

1. Data points with outliers
2. Data points with different densities
3. Data points with round shapes
4. Data points with non-convex shapes

✅ Correct Answer: 4

Hierarchical clustering is slower than non-hierarchical clustering?

1. True
2. false
3. depends on data
4. cannot say

✅ Correct Answer: 1

High entropy means that the partitions in classification are

1. pure
2. not pure
3. useful
4. useless

✅ Correct Answer: 2

Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

1. decision trees
2. density-based clustering
3. model-based clustering
4. k-means clustering

✅ Correct Answer: 2

The main disadvantage of maximum likelihood methods is that they are _____

1. mathematically less folded
2. mathematically less complex
3. mathematically less complex
4. computationally intense

✅ Correct Answer: 4

The maximum likelihood method can be used to explore relationships among more diverse sequences, conditions that are not well handled by maximum parsimony methods.

1. true
2. false
3. -
4. -

✅ Correct Answer: 1

Which Statement is not true statement.

1. k-means clustering is a linear clustering algorithm.
2. k-means clustering aims to partition n observations into k clusters
3. k-nearest neighbor is same as k-means
4. k-means is sensitive to outlier

✅ Correct Answer: 3

what is Feature scaling done before applying K-Mean algorithm?

1. in distance calculation it will give the same weights for all features
2. you always get the same clusters. if you use or don use feature scaling
3. in manhattan distance it is an important step but in euclidian it is not
4. none of these

✅ Correct Answer: 1

With Bayes theorem the probability of hypothesis HÂ¾ specified by P(H) Â¾ is referred to as

1. a conditional probability
2. an a priori probability
3. a bidirectional probability
4. a posterior probability

✅ Correct Answer: 2

The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car

1. 0.0398
2. 0.0389
3. 0.0368
4. 0.0396

✅ Correct Answer: 4

What is the naïve assumption in a Naïve Bayes Classifier.

1. all the classes are independent of each other
2. all the features of a class are independent of each other
3. the most probable feature for a class is the most important feature to be cinsidered for classification
4. all the features of a class are conditionally dependent on each other

✅ Correct Answer: 4

What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?

1. p
2. 2p
3. p(p+1)/2
4. p(p+3)/2

✅ Correct Answer: 4

Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1.

1. 1 is true, 2 is false
2. 1 is false, 2 is true
3. 1 is true, 2 is true
4. 1 is false, 2 is false

✅ Correct Answer: 3

Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.

1. 0
2. 1
3. 0.1
4. 0.9

✅ Correct Answer: 1

Which of the following statements about Naive Bayes is incorrect?

1. attributes are equally important.
2. attributes are statistically dependent of one another given the class value.
3. attributes are statistically independent of one another given the class value.
4. attributes can be nominal or numeric

✅ Correct Answer: 2

How the entries in the full joint probability distribution can be calculated?

1. using variables
2. using information
3. both using variables & information
4. None of the mentioned

✅ Correct Answer: 2

How many terms are required for building a bayes model?

1. 1
2. 2
3. 3
4. 4

✅ Correct Answer: 3

Skewness of Normal distribution is ___________

1. negative
2. positive
3. 0
4. undefined

✅ Correct Answer: 3

The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?

1. the attributes are not linearly related.
2. as the value of one attribute increases the value of the second attribute also increases
3. as the value of one attribute decreases the value of the second attribute increases
4. the attributes show a linear relationship

✅ Correct Answer: 3

8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?

1. c1: (4,4), c2: (2,2), c3: (7,7)
2. c1: (6,6), c2: (4,4), c3: (9,9)
3. c1: (2,2), c2: (0,0), c3: (5,5)
4. c1: (4,4), c2: (3,3), c3: (7,7)

✅ Correct Answer: 4

In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?

1. p(x/c)
2. p(c/x)
3. p(c)
4. p(x)

✅ Correct Answer: 1

Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability

1. 1 and 3
2. B. 2 and 3
3. 1, 2 and 3
4. 1 and 2

✅ Correct Answer: 4

What is back propagation?

1. it is another name given to the curvy function in the perceptron
2. it is the transmission of error back through the network to adjust the inputs
3. it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
4. none of the mentioned

✅ Correct Answer: 1

Which of the following is an application of NN (Neural Network)?

1. sales forecasting
2. data validation
3. risk management
4. All of the mentioned

✅ Correct Answer: 4

Neural Networks are complex ______________ with many parameters.

1. linear functions
2. nonlinear functions
3. discrete functions
4. exponential functions

✅ Correct Answer: 1

Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

1. true – this works always, and these multiple perceptrons learn to classify even complex problems
2. false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
3. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
4. alse – just having a single perceptron is enough

✅ Correct Answer: 3

Which one of the following is not a major strength of the neural network approach?

1. neural network learning algorithms are guaranteed to converge to an optimal solution
2. neural networks work well with datasets containing noisy data
3. neural networks can be used for both supervised learning and unsupervised clustering
4. neural networks can be used for applications that require a time element to be included in the data

✅ Correct Answer: 1

The network that involves backward links from output to the input and hidden layers is called

1. self organizing maps
2. perceptrons
3. recurrent neural network
4. multi layered perceptron

✅ Correct Answer: 3

Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features

1. 1
2. 2
3. 3&4
4. 1,2,3&4

✅ Correct Answer: 4

What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned

1. a
2. b
3. c
4. b&c

✅ Correct Answer: 3

What is the sequence of the following tasks in a perceptron? Initialize weights of perceptron randomly Go to the next batch of dataset If the prediction does not match the output, change the weights For a sample input, compute an output

1. 1, 4, 3, 2
2. 3, 1, 2, 4
3. 4, 3, 2, 1
4. 1, 2, 3, 4

✅ Correct Answer: 1

In which neural net architecture, does weight sharing occur?

1. recurrent neural network
2. convolutional neural network
3. fully connected neural network
4. both a and b

✅ Correct Answer: 4

Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data

1. 1 and 2
2. 2 and 3
3. 1 and 3
4. 1,2 and 3

✅ Correct Answer: 3

The F-test

1. an omnibus test
2. considers the reduction in error when moving from the complete model to the reduced model
3. considers the reduction in error when moving from the reduced model to the complete model
4. can only be conceptualized as a reduction in error

✅ Correct Answer: 3

What is true about an ensembled classifier? 1. Classifiers that are more “sure” can vote with more conviction 2. Classifiers can be more “sure” about a particular part of the space 3. Most of the times, it performs better than a single classifier

1. 1 and 2
2. 1 and 3
3. 2 and 3
4. all of the above

✅ Correct Answer: 4

Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability

1. 1 and 3
2. 2 and 3
3. 1 and 2
4. 1, 2 and 3

✅ Correct Answer: 3

Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces

1. 1
2. 2
3. 1 and 3
4. 1, 2 and 3

✅ Correct Answer: 4

If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?

1. yes
2. no
3. can’t say
4. None of These

✅ Correct Answer: 2

Generally, an ensemble method works better, if the individual base models have ____________?Note: Suppose each individual base models have accuracy greater than 50%.

1. less correlation among predictions
2. high correlation among predictions
3. correlation does not have any impact on ensemble output
4. none of the above

✅ Correct Answer: 1

In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.

1. bagging
2. boosting
3. a or b
4. none of these

✅ Correct Answer: 1

Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

1. 0.05
2. 0.06
3. 0.07
4. 0.09

✅ Correct Answer: 2