730+ Machine Learning (ML) Solved MCQs

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable a system to improve its performance on a specific task over time. In other words, machine learning algorithms are designed to allow a computer to learn from data, without being explicitly programmed.
301.

A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case?

A. feature f1 is an example of nominal variable.
B. feature f1 is an example of ordinal variable.
C. it doesn't belong to any of the above category.
D. both of these
Answer» B. feature f1 is an example of ordinal variable.
302.

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
Answer» A. transform data to zero mean
303.

What is PCA, KPCA and ICA used for?

A. principal components analysis
B. kernel based principal component analysis
C. independent component analysis
D. all above
Answer» D. all above
304.

Can a model trained for item based similarity also choose from a given set of items?

A. yes
B. no
Answer» A. yes
305.

What are common feature selection methods in regression task?

A. correlation coefficient
B. greedy algorithms
C. all above
D. none of these
Answer» C. all above
306.

The parameter            allows specifying the percentage of elements to put into the test/training set

A. test_size
B. training_size
C. all above
D. none of these
Answer» C. all above
307.

In many classification problems, the target             is made up of categorical labels which cannot immediately be processed by any algorithm.

A. random_state
B. dataset
C. test_size
D. all above
Answer» B. dataset
308.

             adopts a dictionary-oriented approach, associating to each category label a progressive integer number.

A. labelencoder class
B. labelbinarizer class
C. dictvectorizer
D. featurehasher
Answer» A. labelencoder class
309.

If Linear regression model perfectly first i.e., train error is zero, then

A. test error is also always zero
B. test error is non zero
C. couldn't comment on test error
D. test error is equal to train error
Answer» C. couldn't comment on test error
310.

Which of the following metrics can be used for evaluating regression models?
i) R Squared
ii) Adjusted R Squared
iii) F Statistics
iv) RMSE / MSE / MAE

A. ii and iv
B. i and ii
C. ii, iii and iv
D. i, ii, iii and iv
Answer» D. i, ii, iii and iv
311.

In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?

A. by 1
B. no change
C. by intercept
D. by its slope
Answer» D. by its slope
312.

Function used for linear regression in R is

A. lm(formula, data)
B. lr(formula, data)
C. lrm(formula, data)
D. regression.linear(formula, data)
Answer» A. lm(formula, data)
313.

In syntax of linear model lm(formula,data,..), data refers to

A. matrix
B. vector
C. array
D. list
Answer» B. vector
314.

In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to

A. (x-intercept, slope)
B. (slope, x-intercept)
C. (y-intercept, slope)
D. (slope, y-intercept)
Answer» C. (y-intercept, slope)
315.

Linear Regression is a supervised machine learning algorithm.

A. true
B. false
Answer» A. true
316.

It is possible to design a Linear regression algorithm using a neural network?

A. true
B. false
Answer» A. true
317.

Overfitting is more likely when you have huge amount of data to train?

A. true
B. false
Answer» B. false
318.

Which of the following statement is true about outliers in Linear regression?

A. linear regression is sensitive to outliers
B. linear regression is not sensitive to outliers
C. can't say
D. none of these
Answer» A. linear regression is sensitive to outliers
319.

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

A. since the there is a relationship means our model is not good
B. since the there is a relationship means our model is good
C. can't say
D. none of these
Answer» A. since the there is a relationship means our model is not good
320.

Naive Bayes classifiers are a collection ------------------of algorithms

A. classification
B. clustering
C. regression
D. all
Answer» A. classification
321.

Naive Bayes classifiers is                              Learning

A. supervised
B. unsupervised
C. both
D. none
Answer» A. supervised
322.

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

A. false
B. true
Answer» B. true
323.

Features being classified is                    of each other in Nave Bayes Classifier

A. independent
B. dependent
C. partial dependent
D. none
Answer» A. independent
324.

Bayes Theorem is given by where 1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.

A. true
B. false
Answer» A. true
325.

In given image, P(H|E) is                  probability.

A. posterior
B. prior
Answer» A. posterior
326.

In given image, P(H) is                  probability.

A. posterior
B. prior
Answer» B. prior
327.

Conditional probability is a measure of the probability of an event given that another

A. true
B. false
Answer» A. true
328.

Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

A. true
B. false
Answer» A. true
329.

Bernoulli Nave Bayes Classifier is                     distribution

A. continuous
B. discrete
C. binary
Answer» C. binary
330.

Multinomial Nave Bayes Classifier is                     distribution

A. continuous
B. discrete
C. binary
Answer» B. discrete
331.

Gaussian Nave Bayes Classifier is                     distribution

A. continuous
B. discrete
C. binary
Answer» A. continuous
332.

Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

A. true
B. false
Answer» A. true
333.

Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the               of the feature values.

A. mean
B. variance
C. discrete
D. random
Answer» A. mean
334.

SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)

A. true
B. false
Answer» B. false
335.

Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

A. true
B. false
Answer» A. true
336.

Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

A. true
B. false
Answer» A. true
337.

SVM is a algorithm

A. classification
B. clustering
C. regression
D. all
Answer» A. classification
338.

SVM is a learning

A. supervised
B. unsupervised
C. both
D. none
Answer» A. supervised
339.

The linearSVMclassifier works by drawing a straight line between two classes

A. true
B. false
Answer» A. true
340.

Which of the following function provides unsupervised prediction ?

A. cl_forecastb
B. cl_nowcastc
C. cl_precastd
D. none of the mentioned
Answer» D. none of the mentioned
341.

Which of the following is characteristic of best machine learning method ?

A. fast
B. accuracy
C. scalable
D. all above
Answer» D. all above
342.

What are the different Algorithm techniques in Machine Learning?

A. supervised learning and semi-supervised learning
B. unsupervised learning and transduction
C. both a & b
D. none of the mentioned
Answer» C. both a & b
343.

What is the standard approach to supervised learning?

A. split the set of example into the training set and the test
B. group the set of example into the training set and the test
C. a set of observed instances tries to induce a general rule
D. learns programs from data
Answer» A. split the set of example into the training set and the test
344.

Which of the following is not Machine Learning?

A. artificial intelligence
B. rule based inference
C. both a & b
D. none of the mentioned
Answer» B. rule based inference
345.

What is Model Selection in Machine Learning?

A. the process of selecting models among different mathematical models, which are used to describe the same data set
B. when a statistical model describes random error or noise instead of underlying relationship
C. find interesting directions in data and find novel observations/ database cleaning
D. all above
Answer» A. the process of selecting models among different mathematical models, which are used to describe the same data set
346.

Which are two techniques of Machine Learning ?

A. genetic programming and inductive learning
B. speech recognition and regression
C. both a & b
D. none of the mentioned
Answer» A. genetic programming and inductive learning
347.

Even if there are no actual supervisors                learning is also based on feedback provided by the environment

A. supervised
B. reinforcement
C. unsupervised
D. none of the above
Answer» B. reinforcement
348.

What does learning exactly mean?

A. robots are programed so that they can perform the task based on data they gather from sensors.
B. a set of data is used to discover the potentially predictive relationship.
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.
Answer» C. learning is the ability to change according to external stimuli and remembering most of all previous experiences.
349.

When it is necessary to allow the model to develop a generalization ability and avoid a common problem called           .

A. overfitting
B. overlearning
C. classification
D. regression
Answer» A. overfitting
350.

Techniques involve the usage of both labeled and unlabeled data is called     .

A. supervised
B. semi-supervised
C. unsupervised
D. none of the above
Answer» B. semi-supervised
351.

In reinforcement learning if feedback is negative one it is defined as       .

A. penalty
B. overlearning
C. reward
D. none of above
Answer» A. penalty
352.

According to        , it's a key success factor for the survival and evolution of all species.

A. claude shannon\s theory
B. gini index
C. darwin's theory
D. none of above
Answer» C. darwin's theory
353.

A supervised scenario is characterized by the concept of a          .

A. programmer
B. teacher
C. author
D. farmer
Answer» B. teacher
354.

overlearning causes due to an excessive            .

A. capacity
B. regression
C. reinforcement
D. accuracy
Answer» A. capacity
355.

Which of the following is an example of a deterministic algorithm?

A. pca
B. k-means
C. none of the above
Answer» A. pca
356.

Which of the following model model include a backwards elimination feature selection routine?

A. mcv
B. mars
C. mcrs
D. all above
Answer» B. mars
357.

Can we extract knowledge without apply feature selection

A. yes
B. no
Answer» A. yes
358.

While using feature selection on the data, is the number of features decreases.

A. no
B. yes
Answer» B. yes
359.

Which of the following are several models

A. regression
B. classification
C. none of the above
Answer» C. none of the above
360.

          provides some built-in datasets that can be used for testing purposes.

A. scikit-learn
B. classification
C. regression
D. none of the above
Answer» A. scikit-learn
361.

While using           all labels are turned into sequential numbers.

A. labelencoder class
B. labelbinarizer class
C. dictvectorizer
D. featurehasher
Answer» A. labelencoder class
362.

             produce sparse matrices of real numbers that can be fed into any machine learning model.

A. dictvectorizer
B. featurehasher
C. both a & b
D. none of the mentioned
Answer» C. both a & b
363.

scikit-learn offers the class           , which is responsible for filling the holes using a strategy based on the mean, median, or frequency

A. labelencoder
B. labelbinarizer
C. dictvectorizer
D. imputer
Answer» D. imputer
364.

Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.

A. minmaxscaler
B. maxabsscaler
C. both a & b
D. none of the mentioned
Answer» C. both a & b
365.

scikit-learn also provides a class for per- sample normalization,          

A. normalizer
B. imputer
C. classifier
D. all above
Answer» A. normalizer
366.

           dataset with many features contains information proportional to the independence of all features and their variance.

A. normalized
B. unnormalized
C. both a & b
D. none of the mentioned
Answer» B. unnormalized
367.

In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the         .

A. concuttent matrix
B. convergance matrix
C. supportive matrix
D. covariance matrix
Answer» D. covariance matrix
368.

The          parameter can assume different values which determine how the data matrix is initially processed.

A. run
B. start
C. init
D. stop
Answer» C. init
369.

           allows exploiting the natural sparsity of data while extracting principal components.

A. sparsepca
B. kernelpca
C. svd
D. init parameter
Answer» A. sparsepca
370.

Which of the following is true about Residuals ?

A. lower is better
B. higher is better
C. a or b depend on the situation
D. none of these
Answer» A. lower is better
371.

Overfitting is more likely when you have huge amount of data to train?

A. true
B. false
Answer» B. false
372.

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

A. since the there is a relationship means our model is not good
B. since the there is a relationship means our model is good
C. can�t say
D. none of these
Answer» A. since the there is a relationship means our model is not good
373.

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

A. you will always have test error zero
B. you can not have test error zero
C. none of the above
Answer» C. none of the above
374.

In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

A. if r squared increases, this variable is significant.
B. if r squared decreases, this variable is not significant.
C. individually r squared cannot tell about variable importance. we can't say anything about it right now.
D. none of these.
Answer» C. individually r squared cannot tell about variable importance. we can't say anything about it right now.
375.

Which of the one is true about Heteroskedasticity?

A. linear regression with varying error terms
B. linear regression with constant error terms
C. linear regression with zero error terms
D. none of these
Answer» A. linear regression with varying error terms
376.

Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free

A. 1,2 and 3.
B. 1,3 and 4.
C. 1 and 3.
D. all of above.
Answer» D. all of above.
377.

To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?

A. scatter plot
B. barchart
C. histograms
D. none of these
Answer» A. scatter plot
378.

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

A. the polynomial degree
B. whether we learn the weights by matrix inversion or gradient descent
C. the use of a constant-term
Answer» A. the polynomial degree
379.

Can we calculate the skewness of variables based on mean and median?

A. true
B. false
Answer» B. false
380.

Which of the following is true about Ridge or Lasso regression methods in case of feature selection?

A. ridge regression uses subset selection of features
B. lasso regression uses subset selection of features
C. both use subset selection of features
D. none of above
Answer» B. lasso regression uses subset selection of features
381.

Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-

A. 1 and 2
B. 1 and 3
C. 2 and 4
D. none of the above
Answer» A. 1 and 2
382.

How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

A. 1
B. 2
C. can't say
Answer» B. 2
383.

Conditional probability is a measure of the probability of an event given that another event has already occurred.

A. true
B. false
Answer» A. true
384.

What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function

A. 1
B. 2
C. 1 and 2
D. none of these
Answer» C. 1 and 2
385.

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?

A. misclassification would happen
B. data will be correctly classified
C. can't say
D. none of these
Answer» A. misclassification would happen
386.

The cost parameter in the SVM means:

A. the number of cross- validations to be made
B. the kernel to be used
C. the tradeoff between misclassification and simplicity of the model
D. none of the above
Answer» C. the tradeoff between misclassification and simplicity of the model
387.

If you remove the non-red circled points from the data, the decision boundary will

A. true
B. false
Answer» B. false
388.

How do you handle missing or corrupted data in a dataset?

A. drop missing rows or columns
B. replace missing values with mean/median/mode
C. assign a unique category to missing values
D. all of the above
Answer» D. all of the above
389.

The SVMs are less effective when:

A. the data is linearly separable
B. the data is clean and ready to use
C. the data is noisy and contains overlapping points
Answer» C. the data is noisy and contains overlapping points
390.

If there is only a discrete number of possible outcomes called          .

A. modelfree
B. categories
C. prediction
D. none of above
Answer» B. categories
391.

Some people are using the term        instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

A. inference
B. interference
C. accuracy
D. none of above
Answer» A. inference
392.

The term           can be freely used, but with the same meaning adopted in physics or system theory.

A. accuracy
B. cluster
C. regression
D. prediction
Answer» D. prediction
393.

Common deep learning applications / problems can also be solved using        

A. real-time visual object identification
B. classic approaches
C. automatic labeling
D. bio-inspired adaptive systems
Answer» B. classic approaches
394.

Identify the various approaches for machine learning.

A. concept vs classification learning
B. symbolic vs statistical learning
C. inductive vs analytical learning
D. all above
Answer» D. all above
395.

what is the function of Unsupervised Learning?

A. find clusters of the data and find low-dimensional representations of the data
B. find interesting directions in data and find novel observations/ database cleaning
C. interesting coordinates and correlations
D. all
Answer» D. all
396.

What are the two methods used for the calibration in Supervised Learning?

A. platt calibration and isotonic regression
B. statistics and informal retrieval
Answer» A. platt calibration and isotonic regression
397.

Which of the following are several models for feature extraction

A. regression
B. classification
C. none of the above
Answer» C. none of the above
398.

Let's say, a Linear regression model perfectly fits the training data (train error

A. you will always have test error zero
B. you can not have test error zero
C. none of the above
Answer» C. none of the above
399.

Which of the following assumptions do we make while deriving linear regression parameters?
1. The true relationship between dependent y and predictor x is linear
2. The model errors are statistically independent
3. The errors are normally distributed with a 0 mean and constant standard deviation
4. The predictor x is non-stochastic and is measured error-free

A. 1,2 and 3.
B. 1,3 and 4.
C. 1 and 3.
D. all of above.
Answer» D. all of above.
400.

Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). 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?

A. it is more likely for x1 to be excluded from the model
B. it is more likely for x1 to be included in the model
C. can't say
D. none of these
Answer» B. it is more likely for x1 to be included in the model
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