Machine Learning Multiple Choice Questions and Answers 2023 (With Explanation) | Machine Learning MCQ Questions

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Machine Learning Multiple Choice Questions and Answers 2023 | Machine Learning Questions and Answers | Machine Learning MCQ Quiz Questions and Answers | Machine Learning MCQ Questions and Answers PDF Download

Machine Learning Multiple Choice Questions and Answers 2023 | Machine Learning Quiz Questions & Answers:  Machine Learning is an area of computer science that allows machines to improve at certain tasks over time. It’s one of the finest AI applications since it allows computers to learn and improve without being explicitly programmed. Another thing to keep in mind is that any machine learning approach is labeled AI. Yet, not every AI can be classified as machine learning. Human knowledge is earned mostly via life experience. Machines get the knowledge that is necessary to be fed by gathering vast volumes of information about a given application and feeding it to them in a very short period of time. The steps in the machine learning lifecycle are as follows: data collection, preparation, analysis, data wrangling, model training, model testing, and deployment, which is the most crucial and last phase. Get the latest Question Papers Here


Machine Learning Multiple Choice Questions With Solutions

What is Machine learning?

  1. The autonomous acquisition of knowledge through the use of computer programs
  2. The autonomous acquisition of knowledge through the use of manual programs
  3. The selective acquisition of knowledge through the use of computer programs
  4. The selective acquisition of knowledge through the use of manual programs

Answer: A

Explanation: “Machine learning” is the autonomous acquisition of knowledge through the use of computer programs.

What is true about Machine Learning?

  1. Machine Learning (ML) is the field of computer science
  2. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method
  3. The main focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention
  4. All of the above

Answer: D

Explanation: All the statements are true about Machine Learning.

ML is a field of AI consisting of learning algorithms that?

  1. Improve their performance
  2. At executing some task
  3. Over time with experience
  4. All of the above

Answer: D

Explanation: Machine learning is a field of AI consisting of learning algorithms that: Improve their performance (P), At executing some task (T), Over time with experience (E).

Different learning methods do not include?

  1. Memorization
  2. Analogy
  3. Introduction
  4. Deduction

Answer: C

Explanation: Different learning methods in the ML do not include Introdution.

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

  1. Decision Tree
  2. Random Forest
  3. Regression
  4. Classification

Answer: B

Explanation: Random Forest

High entropy means that the partitions in classification are

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

Answer: B

Explanation: Entropy is a measure of the randomness in the information being processed So the higher the entropy, the harder it is to draw any conclusions from that information. Entropy is a measure of disorder or purity or unpredictability or uncertainty. So Low entropy means less uncertain and high entropy means more uncertain.

Which of the following are ML methods?

  1. Based on human supervision
  2. Supervised Learning
  3. Semi-reinforcement Learning
  4. All of the above

Answer: A

Explanation: The following are various Machine learning methods based on some broad categories: Based on human supervision, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning.

In language understanding, the levels of knowledge do not include?

  1. Phonological
  2. Syntactic
  3. Empirical
  4. Logical

Answer: C

Explanation: In language understanding, the levels of knowledge do not include empirical knowledge.

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?

  1. 12
  2. 24
  3. 48
  4. 72

Answer: D

Explanation: Maximum possible different examples are the products of the possible values of each attribute and the number of classes so the result would be
3 * 2 * 2 * 2 * 3 = 72

When performing regression or classification, which of the following is the correct way to preprocess the data?

  1. Normalize the data → PCA → training
  2. PCA → normalize PCA output → training
  3. Normalize the data → PCA → normalize PCA output → training
  4. None of the above

Answer: A

Explanation: First Normalize the data then PCA then training.

Machine Learning MCQs and Answers 2023

Type of matrix decomposition model is_____________

  1. predictive model
  2. descriptive model
  3. logical model
  4. None

Answer: Descriptive Model

PCA is_________________

  1. backward feature selection
  2.  forward feature selection
  3. feature extraction
  4.  None of these

Answer:  Feature Extraction

Database query is used to uncover this type of knowledge.

  1. hidden
  2. shallow
  3. deep
  4. multidimensional

Answer: multidimensional

Data used to build a data mining model.

  1. training data
  2. hidden data
  3. test data
  4. validation data

Answer: training data

Application of machine learning methods to large databases is called_______

  1. big data computing
  2. artificial intelligence
  3. data mining
  4. internet of things

Answer: data mining

Which learning Requires Self Assessment to identify patterns within data?

  1. supervised learning
  2. unsupervised learning
  3. semi supervised learning
  4. reinforced learning

Answer: unsupervised learning

What does dimensionality reduction reduce?

  1. collinearity
  2. stochastic
  3. entropy
  4. performance

Answer: collinearity

Some telecommunication company wants to segment their customers into distinct groups ,this is an example of___

  1. supervised learning
  2. unsupervised learning
  3. data extraction
  4. reinforcement learning

Answer: unsupervised learning

Which of the following is the best machine learning method?

  1. accuracy
  2. scalable
  3. fast
  4. All of above

Answer: All of above

In multiclass classification number of classes must be____

  1. equals to two
  2. less than two
  3. greater than two
  4. None

Answer: greater than two

Which of the following can only be used when training data are linearly separable?

  1. linear logistic regression
  2. linear hard-margin svm
  3. linear soft margin svm
  4. parzen windows

Answer: linear hard-margin svm

Which of the following can only be used when training data are linearly separable?

  1. linear logistic regression
  2. linear soft margin svm
  3. linear hard-margin svm
  4. the centroid method

Answer: linear hard-margin svm

You are given seismic data and you want to predict next earthquake , this is an example of_____

  1. supervised learning
  2. unsupervised learning
  3. reinforcement learning
  4. dimensionality reduction

Answer: supervised learning

Prediction is______________

  1. discipline in statistics used to find projections in multidimensional data
  2. value entered in database by expert
  3. the result of application of specific theory or rule in a specific case
  4. independent of data

Answer: the result of application of specific theory or rule in a specific case

Impact of high variance on the training set?

  1. underfitting
  2. overfitting
  3. both underfitting & overfitting
  4. depends upon the dataset

Answer: overfitting

Which of the following is an example of feature extraction?

  1.  applying pca to project high dimensional data
  2. construction bag of words from an email
  3. removing stop words
  4.  forward selection

Answer:  applying pca to project high dimensional data

The effectiveness of an SVM depends upon___

  1. kernel parameters
  2. selection of kernel
  3. soft margin parameter
  4.  All of the above

Answer: selection of kernel

What do you mean by a hard margin?

  1. the svm allows very low error in classification
  2. the svm allows high amount of error in classification
  3. All of above
  4. None of above

Answer: the svm allows very low error in classification

Supervised learning and unsupervised clustering both require which is correct according to the statement

  1.  input attribute
  2.  hidden attribute
  3. output attribute
  4. categorical attribute

Answer: input attribute

Following are the types of supervised learning________

  1. regression
  2. classification
  3. subgroup discovery
  4. All of above

Answer: All of above

A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is_______________

  1. ordinal
  2. nominal
  3. categorical
  4. boolean

Answer: ordinal

Following is powerful distance metrics used by Geometric model______

  1.  manhattan distance
  2. euclidean distance
  3. All of above
  4. None of above

Answer: All of above

The output of training process in machine learning is________________

  1. machine learning algorithm
  2. machine learning model
  3. null
  4. accuracy

Answer: machine learning model

Which of the following is a good test dataset characteristic?

  1.  is representative of the dataset as a whole
  2. large enough to yield meaningful results
  3. All of above
  4. None of above

Answer: All of above

Support Vector Machine is________

  1. geometric model
  2. probabilistic model
  3. logical model
  4. none

Answer: geometric model

Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up. Specify what type of machine learning is best suited?

  1. regression
  2. means algorithm
  3. reinforcement learning
  4. None

Answer: reinforcement learning

In simple term, machine learning is______

  1. prediction to answer a query
  2. training based on historical data
  3. All of above
  4. None of above

Answer: All of above

Which of the following techniques would perform better for reducing dimensions of a data set?

  1. removing columns which have high variance in data
  2. removing columns which have too many missing value
  3. removing columns with dissimilar data trends
  4. None of the above

Answer: removing columns which have too many missing values

What characterize is hyperplane in geometrical model of machine learning?

  1. a plane with 1 dimensional fewer than number of input attributes
  2. a plane with 1 dimensional more than number of input attributes
  3. a plane with 2 dimensional more than number of input attributes
  4. a plane with 2 dimensional fewer than number of input attributes

Answer: a plane with 2 dimensional fewer than number of input attributes

You are given reviews of few Netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of________

  1. unsupervised learning
  2. semi supervised learning
  3. supervised learning
  4. reinforcement learning

Answer: supervised learning

Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification

  1. deductive
  2. exampler
  3. classical
  4. inductive

Answer: inductive

The problem of finding hidden structure in unlabeled data is called______

  1. unsupervised learning
  2. reinforcement learning
  3. supervised learning
  4. None

Answer: unsupervised learning

If machine learning model output involves target variable then that model is called as_______

  1. predictive model
  2. descriptive model
  3. reinforcement learning
  4. All of above

Answer: predictive model

Which of the following is a reasonable way to select the number of principal components “k”?

  1. choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer)
  2. choose k to be the smallest value so that at least 99% of the variance is retained
  3. choose k to be the largest value so that 99% of the variance is retained
  4. use the elbow method

Answer: choose k to be the smallest value so that at least 99% of the variance is retained

A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?

  1. variable f1 is an example of ordinal variable
  2. it doesn\t belong to any of the mentioned categories
  3. variable f1 is an example of nominal variable
  4. it belongs to both ordinal and nominal category

Answer: variable f1 is an example of ordinal variable

What is the purpose of the Kernel Trick?

  1. to transform the problem from regression to classification
  2. to transform the problem from supervised to unsupervised learning.
  3. to transform the data from nonlinearly separable to linearly separable
  4. All of above

Answer:  to transform the data from nonlinearly separable to linearly separable

Feature can be used as a______

  1. predictor
  2. binary split
  3. All of above
  4. None of above

Answer: All of above

What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?

  1. high variance
  2. low variance
  3. faster runtime compared to k-fold cross validation
  4. slower runtime compared to normal validation

Answer: high variance

The cost parameter in the SVM means_______

  1. the kernel to be used
  2. the tradeoff between misclassification and simplicity of the model
  3. the number of cross-validations to be made
  4. None

Answer: the tradeoff between misclassification and simplicity of the model

Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

  1. accuracy
  2. auc-roc
  3. logloss
  4. mean-squared-error

Answer: mean-squared-error

A measurable property or parameter of the data-set is_____

  1. training data
  2. test data
  3. feature
  4. validation data

Answer: feature

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