# 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.
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401.

## 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
402.

## Which of the following statement(s) can

A. 1 and 2
B. 1 and 3
C. 2 and 4
D. none of the above
403.

A. 1 and 2
B. 1 and 3.
C. 2 and 3.
D. 1,2 and 3.
404.

A. yes
B. no
405.

## Which of the following option is true regarding Regression and Correlation ?Note: y is dependent variable and x is independent variable.

A. the relationship is symmetric between x and y in both.
B. the relationship is not symmetric between x and y in both.
C. the relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Answer» D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
406.

A. yes
B. no
407.

A. true
B. false
408.

## When the C parameter is set to infinite, which of the following holds true?

A. the optimal hyperplane if exists, will be the one that completely separates the data
B. the soft-margin classifier will separate the data
C. none of the above
Answer» A. the optimal hyperplane if exists, will be the one that completely separates the data
409.

## 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 large value of C(C->infinity)?

A. we can still classify data correctly for given setting of hyper parameter c
B. we can not classify data correctly for given setting of hyper parameter c
C. can�t say
D. none of these
Answer» A. we can still classify data correctly for given setting of hyper parameter c
410.

A. true
B. false
411.

A. true
B. false
412.

A. usual
B. decision
C. parallel
413.

## The          of the hyperplane depends upon the number of features.

A. dimension
B. classification
C. reduction
414.

A. true
B. false
415.

A. true
B. false
416.

A. true
B. false
417.

A. true
B. false
418.

## What is the purpose of performing cross- validation?

A. to assess the predictive performance of the models
B. to judge how the trained model performs outside the sample on test data
C. both a and b
Answer» C. both a and b
419.

## Which of the following is true about Naive Bayes ?

A. assumes that all the features in a dataset are equally important
B. assumes that all the features in a dataset are independent
C. both a and b
D. none of the above option
Answer» C. both a and b
420.

## Which of the following isnotsupervised learning?

A. ��pca
B. ��decision tree
C. ��naive bayesian
D. linerar regression
421.

## can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

A. supervised
B. semi-supervised
C. reinforcement
D. clusters
422.

A. overfitting
B. overlearning
C. reward
D. none of above
423.

## In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called         .

A. deep learning
B. machine learning
C. reinforcement learning
D. unsupervised learning
424.

A. regression
B. accuracy
C. modelfree
D. scalable
425.

## showed better performance than other approaches, even without a context- based model

A. machine learning
B. deep learning
C. reinforcement learning
D. supervised learning
426.

A. yes
B. no
427.

A. true
B. false
428.

## 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
429.

## 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
430.

## 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
431.

A. matrix
B. vector
C. array
D. list
432.

A. true
B. false
433.

A. true
B. false
434.

## Which of the following methods do we use to find the best fit line for data in Linear Regression?

A. least square error
B. maximum likelihood
C. logarithmic loss
D. both a and b
435.

## Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. 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
Answer» C. 1 is true and 2 is false
436.

A. 1 and 2
B. 1 and 3.
C. 2 and 3.
D. 1,2 and 3.
437.

## Which of the following option is true regarding Regression and Correlation ?Note: y is dependent variable and x is independent variable.

A. the relationship is symmetric between x and y in both.
B. the relationship is not symmetric between x and y in both.
C. the relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Answer» D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
438.

A. by 1
B. no change
C. by intercept
D. by its slope
439.

## Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression

A. 1 and 2
B. only 1
C. only 2
D. none of these.
440.

A. 1
B. 2
C. 3
D. 4
441.

A. true
B. false
442.

## Which of the following are real world applications of the SVM?

A. text and hypertext categorization
B. image classification
C. clustering of news articles
D. all of the above
Answer» D. all of the above
443.

A. true
B. false
444.

A. 0.4
B. 0.64
C. 0.29
D. 0.75
445.

A. true
B. false
446.

A. overfitting
B. overlearning
C. reward
D. none of above
447.

## Which of the following sentence is correct?

A. machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed.
B. data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns.
C. both a & b
D. none of the above
Answer» C. both a & b
448.

## Reinforcement learning is particularly

A. the environment is not
B. it\s often very dynamic
C. it\s impossible to have a
D. all above
449.

## Lets say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?

A. all categories of categorical variable are not present in the test dataset.
B. frequency distribution of categories is different in train as compared to the test dataset.
C. train and test always have same distribution.
D. both a and b
Answer» D. both a and b
450.

## Which of the following sentence is FALSE regarding regression?

A. it relates inputs to outputs.
B. it is used for prediction.
C. it may be used for interpretation.
D. it discovers causal relationships.
Answer» D. it discovers causal relationships.
451.

## Which of the following method is used to find the optimal features for cluster analysis

A. k-means
B. density-based spatial clustering
C. spectral clustering find clusters
D. all above
452.

## scikit-learn also provides functions for creating dummy datasets from scratch:

A. make_classification()
B. make_regression()
C. make_blobs()
D. all above
453.

A. make_blobs
B. random_state
C. test_size
D. training_size
454.

A. 1
B. 2
C. 3
D. 4
455.

## In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.

A. labelencoder class
B. dictvectorizer
C. labelbinarizer class
D. featurehasher
456.

## is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

A. removing the whole line
B. creating sub-model to predict those features
C. using an automatic strategy to input them according to the other known values
D. all above
Answer» A. removing the whole line
457.

## It's possible to specify if the scaling process must include both mean and standard deviation using the parameters              .

A. with_mean=true/false
B. with_std=true/false
C. both a & b
D. none of the mentioned
Answer» C. both a & b
458.

## Which of the following selects the best K high-score features.

A. selectpercentile
B. featurehasher
C. selectkbest
D. all above
459.

## How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same1. In case of fewer observations, it is easy to overfit the data.2. In case of fewer observations, it is hard to overfit the data.3. In case of more observations, it is easy to overfit the data.4. In case of more observations, it is hard to overfit the data.

A. 1 and 4
B. 2 and 3
C. 1 and 3
D. none of theses
460.

## Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

A. in case of very large lambda; bias is low, variance is low
B. in case of very large lambda; bias is low, variance is high
C. in case of very large lambda; bias is high, variance is low
D. in case of very large lambda; bias is high, variance is high
Answer» C. in case of very large lambda; bias is high, variance is low
461.

A. 1 and 3
B. 1 and 4
C. 2 and 3
D. 2 and 4
462.

## Which of the following method(s) does not have closed form solution for its coefficients?

A. ridge regression
B. lasso
C. both ridge and lasso
D. none of both
463.

## Function used for linear regression in R

A. lm(formula, data)
B. lr(formula, data)
C. lrm(formula, data)
D. regression.linear(formula,
464.

## In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to

A. (x-intercept, slope)
B. (slope, x-intercept)
C. (y-intercept, slope)
D. (slope, y-intercept)
465.

## Suppose that we have N independent variables (X1,X2 Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of its variable(Say X1) with Y is -0.95.Which of the following is true for X1?

A. relation between the x1 and y is weak
B. relation between the x1 and y is strong
C. relation between the x1 and y is neutral
D. correlation can�t judge the relationship
Answer» B. relation between the x1 and y is strong
466.

## We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?

A. increase
B. decrease
C. remain constant
D. can't say
467.

## We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?

A. bias increases and variance increases
B. bias decreases and variance increases
C. bias decreases and variance decreases
D. bias increases and variance decreases
Answer» D. bias increases and variance decreases
468.

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. 1, 2 and 3
469.

A. true
B. false
470.

A. 0.4
B. 0.64
C. 0.36
D. 0.5
471.

## Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?

A. you want to increase your data points
B. you want to decrease your data points
C. you will try to calculate more variables
D. you will try to reduce the features
Answer» C. you will try to calculate more variables
472.

## The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVMs?

A. large datasets
B. small datasets
C. medium sized datasets
D. size does not matter
473.

## What do you mean by generalization error in terms of the SVM?

A. how far the hyperplane is from the support vectors
B. how accurately the svm can predict outcomes for unseen data
C. the threshold amount of error in an svm
Answer» B. how accurately the svm can predict outcomes for unseen data
474.

A. 1
B. 1 and 2
C. 1 and 3
D. 2 and 3
475.

A. true
B. false
476.

## If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?

A. underfitting
B. nothing, the model is perfect
C. overfitting
477.

## What is the purpose of performing cross- validation?

A. to assess the predictive performance of the models
B. to judge how the trained model performs outside the
C. both a and b
Answer» C. both a and b
478.

A. yes
B. no
479.

A. true
B. false
480.

A. 0.5
B. 0.26
C. 0.73
D. 0.6
481.

A. 0.4
B. 0.2
C. 0.6
D. 0.45
482.

A. true
B. false
483.

## What are the different Algorithm techniques in Machine Learning?

A. supervised learning and semi-
B. unsupervised learning and transduction
C. both a & b
D. none of the mentioned
Answer» C. both a & b
484.

## can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to

A. supervised
B. semi- supervised
C. reinforcement
D. clusters
485.

A. overfitting
B. overlearning
C. reward
D. none of above
486.

## In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called          .

A. deep learning
B. machine learning
C. reinforcement learning
D. unsupervised learning
487.

## What does learning exactly mean?

A. robots are programed so that they can
B. a set of data is used to discover the
C. learning is the ability to change
D. it is a set of data is used to discover the
Answer» C. learning is the ability to change
488.

## 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
489.

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

A. supervised
B. semi- supervised
C. unsupervised
D. none of the above
490.

A. regression
B. accuracy
C. modelfree
D. scalable
491.

## showed better performance than other approaches, even without a context-based model

A. machine learning
B. deep learning
C. reinforcement learning
D. supervised learning
492.

## Which of the following sentence is correct?

A. machine learning relates with the study,
B. data mining can be defined as the process
C. both a & b
D. none of the above
Answer» C. both a & b
493.

## What is ‘Overfitting’ in Machine learning?

A. when a statistical model describes random error or noise instead of
B. robots are programed so that they can perform the task based on data they gather from
C. while involving the process of learning ‘overfitting’ occurs.
D. a set of data is used to discover the potentially predictive relationship
Answer» A. when a statistical model describes random error or noise instead of
494.

## What is ‘Test set’?

A. test set is used to test the accuracy of the hypotheses generated by the learner.
B. it is a set of data is used to discover the potentially predictive relationship.
C. both a & b
D. none of above
Answer» A. test set is used to test the accuracy of the hypotheses generated by the learner.
495.

## what is the function of ‘Supervised Learning’?

A. classifications, predict time series, annotate strings
B. speech recognition, regression
C. both a & b
D. none of above
Answer» C. both a & b
496.

## Commons unsupervised applications include

A. object segmentation
B. similarity detection
C. automatic labeling
D. all above
497.

## Reinforcement learning is particularly efficient when                             .

A. the environment is not completely deterministic
B. it\s often very dynamic
C. it\s impossible to have a precise error measure
D. all above
498.

## During the last few years, many              algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

A. logical
B. classical
C. classification
D. none of above
499.

## Common deep learning applications include

A. image classification, real-time visual tracking
B. autonomous car driving, logistic optimization
C. bioinformatics, speech recognition
D. all above
500.

## if there is only a discrete number of possible outcomes (called categories), the process becomes a            .

A. regression
B. classification.
C. modelfree
D. categories