Weekly Quiz 2

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

• TRUE

• FALSE

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

• AUC-ROC

• Accuracy

• Logloss

• Mean-Squared-Error

Lasso Regularization can be used for variable selection in Linear Regression.

• True

• False

Which of the following is true about Residuals ?

• Lower is better

• Higher is better

• A or B depend on the situation

• None of these

Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data.You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.

Which of the following is true for X1?

• Relation between the X1 and Y is weak

• Relation between the X1 and Y is strong

• Relation between the X1 and Y is neutral

• Correlation can’t judge the relationship

Looking at above two characteristics, which of the following option is the correct for Pearson correlation between V1 and V2?

If you are given the two variables V1 and V2 and they are following below two characteristics.

1. If V1 increases then V2 also increases

2. If V1 decreases then V2 behavior is unknown

• Pearson correlation will be close to 1

• Pearson correlation will be close to -1

• Pearson correlation will be close to 0

• None of these

Which of the following offsets, do we use in linear regression’s least square line fit? Suppose horizontal axis is independent variable and vertical axis is dependent variable.

• Vertical offset

• Perpendicular offset

• Both, depending on the situation

• None of above

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 don’t have to choose the learning rate

2. It becomes slow when number of features is very large 3. Thers is no need to iterate

• 1 and 2

• 1 and 3

• 2 and 3

• 1,2 and 3

Which of the following statement is true about outliers in Linear regression?

• Linear regression is sensitive to outliers

• Linear regression is not sensitive to outliers

• Can’t say

• None of these

Describe the correlation in the graph shown.

• Strong Negative

• No Correlation

• Weak Negative

• Strong Positive