Q1. True- False: Overfitting is more likely when you have a huge amount of data to train?

True

False

Q2. Which of the following statement is true about the sum of residuals of A and B?

Below graphs show two fitted regression lines (A & B) on randomly generated data. Now, I want to find the sum of residuals in both cases A and B.Note:1.

Note:1. The scale is same in both graphs for both axis.2. The x-axis is independent variable and Y-axis is the dependent variable.

A has higher sum of residuals than B

A has lower sum of residual than B

Both have same sum of residuals

None of these

Q3. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

Choose the option which describes bias in best manner:

In case of very large x; bias is low

In case of very large x; bias is high

We can’t say about bias

None

Q4. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

What will happen when you apply a very large penalty?

Some of the coefficient will become absolute zero

Some of the coefficient will approach zero but not absolute zero

Both A and B depending on the situation

None of these

Q5. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

What will happen when you apply very large penalty in case of Lasso?

Some of the coefficient will become zero

Some of the coefficient will be approaching to zero but not absolute zero

Both A and B depending on the situation

None

Q6. 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

Cannot say

None

Q7. Suppose you plotted a scatter plot of the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

Since the there is a relationship means our model is not good

Since the there is a relationship means our model is good

It is irrelevant to consider

Q8. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another term, it perfectly fits the data.

What will happen when you fit degree 4 polynomial in linear regression?

There are high chances that degree 4 polynomial will over fit the data

There are high chances that degree 4 polynomial will under fit the data

Can’t say

Q9. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another term, it perfectly fits the data.

What will happen when you fit degree 2 polynomial in linear regression?

It is high chances that degree 2 polynomial will over fit the data

It is high chances that degree 2 polynomial will under fit the data

Cannot Say

None

Q10. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.

In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial?

Bias will be high, variance will be high

Bias will be low, variance will be high

Bias will be high, variance will be low

Bias will be low, variance will be low

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