Machine Learning Set 13

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?
Q2 | What would you do in PCA to get the same projection as SVD?
Q3 | What is PCA, KPCA and ICA used for?
Q4 | Can a model trained for item based similarity also choose from a given set of items?
Q5 | What are common feature selection methods in regression task?
Q6 | The parameter            allows specifying the percentage of elements to put into the test/training set
Q7 | In many classification problems, the target             is made up of categorical labels which cannot immediately be processed by any algorithm.
Q8 |              adopts a dictionary-oriented approach, associating to each category label a progressive integer number.
Q9 | Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE
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?
Q11 | Function used for linear regression in R is
Q12 | In syntax of linear model lm(formula,data,..), data refers to
Q13 | In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to
Q14 | Linear Regression is a supervised machine learning algorithm.
Q15 | It is possible to design a Linear regression algorithm using a neural network?
Q16 | Which of the following is true about Residuals ?
Q17 | Overfitting is more likely when you have huge amount of data to train?
Q18 | Naive Bayes classifiers are a collection------------------of algorithms
Q19 | Naive Bayes classifiers is                             Learning
Q20 | Features being classified is independent of each other in Nave Bayes Classifier