Machine Learning Set 18

Q1 | SVMalgorithmsusea set of mathematical functions that are defined as thekernel.

Q2 | In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

Q3 | In SVR we try to fit the error within a

Q4 | Which of the following is true about Naive Bayes ?

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.

Q6 | In reinforcement learning, this feedback is

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         .

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           

Q9 |             showed better performance than other approaches, even without a context- based model

Q10 | If two variables are correlated, is it necessary that they have a linear relationship?

Q11 | Correlated variables can have zero correlation coeffficient. True or 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?

Q13 | Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE

Q14 | In syntax of linear model lm(formula,data,..), data refers to             

Q15 | Linear Regression is a supervised machine learning algorithm.

Q16 | It is possible to design a Linear regression algorithm using a neural network?

Q17 | Which of the following methods do we use to find the best fit line for data in Linear Regression?

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?

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

Q20 | Which of the following option is true regarding Regression andCorrelation ?Note: y is dependent variable and x is independent variable.

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?

Q22 | Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression