Machine Learning Set 17

Q1 | What are the two methods used for the calibration in Supervised Learning?

Q2 | Which of the following are several models for feature extraction

Q3 | Lets say, a Linear regression model perfectly fits the training data (train error

Q4 | Which of the one is true about Heteroskedasticity?

Q5 | Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free

Q6 | To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?

Q7 | Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression

Q8 | 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?

Q9 | 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?

Q10 | Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?

Q11 | Which of the following statement(s) can

Q12 | 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

Q13 | If two variables are correlated, is it necessary that they have a linear relationship?

Q14 | Correlated variables can have zero correlation coeffficient. True or False?

Q15 | Which of the following option is true regarding Regression andCorrelation ?Note: y is dependent variable and x is independent variable.

Q16 | Suppose you are using a Linear SVM classifier with 2 class classification

Q17 | If you remove the non-red circled points from the data, the decision boundary will change?

Q18 | When the C parameter is set to infinite, which of the following holds true?

Q19 | Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?

Q20 | SVM can solvelinearand non- linearproblems

Q21 | The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N the number of features) that distinctly classifies the data points.

Q22 | Hyperplanes are                        boundaries that help classify the data points.

Q23 | The          of the hyperplane depends upon the number of features.

Q24 | Hyperplanes are decision boundaries that help classify the data points.