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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 13
Q1 | 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?
- feature f1 is an example of nominal variable.
- feature f1 is an example of ordinal variable.
- it doesnt belong to any of the above category.
- both of these
Q2 | What would you do in PCA to get the same projection as SVD?
- transform data to zero mean
- transform data to zero median
- not possible
- none of these
Q3 | What is PCA, KPCA and ICA used for?
- principal components analysis
- kernel based principal component analysis
- independent component analysis
- all above
Q4 | Can a model trained for item based similarity also choose from a given set of items?
- yes
- no
Q5 | What are common feature selection methods in regression task?
- correlation coefficient
- greedy algorithms
- all above
- none of these
Q6 | The parameter allows specifying the percentage of elements to put into the test/training set
- test_size
- training_size
- all above
- none of these
Q7 | In many classification problems, the target is made up of categorical labels which cannot immediately be processed by any algorithm.
- random_state
- dataset
- test_size
- all above
Q8 | adopts a dictionary-oriented approach, associating to each category label a progressive integer number.
- labelencoder class
- labelbinarizer class
- dictvectorizer
- featurehasher
Q9 | Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE
- a) ii and iv
- b) i and ii
- c) ii, iii and iv
- d) i, ii, iii and iv
Q10 | 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
Q11 | 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)
Q12 | In syntax of linear model lm(formula,data,..), data refers to
- a) matrix
- b) vector
- c) array
- d) list
Q13 | 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)
Q14 | Linear Regression is a supervised machine learning algorithm.
- a) true
- b) false
Q15 | It is possible to design a Linear regression algorithm using a neural network?
- a) true
- b) false
Q16 | 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
Q17 | Overfitting is more likely when you have huge amount of data to train?
- a) true
- b) false
Q18 | Naive Bayes classifiers are a collection------------------of algorithms
- classification
- clustering
- regression
- all
Q19 | Naive Bayes classifiers is Learning
- supervised
- unsupervised
- both
- none
Q20 | Features being classified is independent of each other in Nave Bayes Classifier
- false
- true