On This Page

This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 18

Q1 | SVMalgorithmsusea set of mathematical functions that are defined as thekernel.
  • true
  • false
Q2 | In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.
  • true
  • false
Q3 | In SVR we try to fit the error within a
  • true
  • false
Q4 | 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
Q5 |            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.
  • supervised
  • semi-supervised
  • reinforcement
  • clusters
Q6 | In reinforcement learning, this feedback is
  • overfitting
  • overlearning
  • reward
  • none of above
Q7 | In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called         .
  • deep learning
  • machine learning
  • reinforcement learning
  • unsupervised learning
Q8 | there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called           
  • regression
  • accuracy
  • modelfree
  • scalable
Q9 |             showed better performance than other approaches, even without a context- based model
  • machine learning
  • deep learning
  • reinforcement learning
  • supervised learning
Q10 | If two variables are correlated, is it necessary that they have a linear relationship?
  • yes
  • no
Q11 | Correlated variables can have zero correlation coeffficient. True or False?
  • true
  • false
Q12 | 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?
  • it is more likely for x1 to be excluded from the model
  • it is more likely for x1 to be included in the model
  • cant say
  • none of these
Q13 | Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE
  • ii and iv
  • i and ii
  • ii, iii and iv
  • i, ii, iii and iv
Q14 | In syntax of linear model lm(formula,data,..), data refers to             
  • matrix
  • vector
  • array
  • list
Q15 | Linear Regression is a supervised machine learning algorithm.
  • true
  • false
Q16 | It is possible to design a Linear regression algorithm using a neural network?
  • true
  • false
Q17 | Which of the following methods do we use to find the best fit line for data in Linear Regression?
  • least square error
  • maximum likelihood
  • logarithmic loss
  • both a and b
Q18 | 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?
  • both are false
  • 1 is false and 2 is true
  • 1 is true and 2 is false
  • both are true
Q19 | We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation. Which of the following is/are true about Normal Equation?1. We dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
  • 1 and 2
  • 1 and 3.
  • 2 and 3.
  • 1,2 and 3.
Q20 | Which of the following option is true regarding Regression andCorrelation ?Note: y is dependent variable and x is independent variable.
  • the relationship is symmetric between x and y in both.
  • the relationship is not symmetric between x and y in both.
  • the relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
  • the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Q21 | In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
  • by 1
  • no change
  • by intercept
  • by its slope
Q22 | Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression
  • 1 and 2
  • only 1
  • only 2
  • none of these.