Free download in PDF Regression Multiple Choice Questions and Answers for competitive exams. These short objective type questions with answers are very important for Board exams as well as competitive exams. These short solved questions or quizzes are provided by Gkseries.
1
In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.
Which of the following option is true?
A
If R Squared increases, this variable is significant
B
If R Squared decreases, this variable is not significant
C
Individually R squared cannot tell about variable importance. We can’t say anything about it right now
Answer: The strength of the relationship between the x and y variables
5
Suppose, we are using Logistic regression model for n-class classification problem. In this case, we can use One-vs-rest method. Choose which of the following option is true regarding this?
A
We need to fit n model in n-class classification problem
B
We need to fit n-1 models to classify into n classes
C
We need to fit only 1 model to classify into n classes
Answer: We need to fit n model in n-class classification problem
6
Logistic Regression transforms the output probability to be in a range of [0, 1]. Which of the following function is used by logistic regression to convert the probability in the range between [0,1].
11
Suppose we have generated the data with help of polynomial regression of degree 3 (degree 3 will perfectly fit this data). Now consider below points and choose the option based on these points.
1. Simple Linear regression will have high bias and low variance
2. Simple Linear regression will have low bias and high variance
3. polynomial of degree 3 will have low bias and high variance
4. Polynomial of degree 3 will have low bias and Low variance
12
Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100). 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?
A
It is more likely for X1 to be excluded from the model
B
It is more likely for X1 to be included in the model
Answer: The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric
16
Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
A
In case of very large lambda; bias is low, variance is low
B
In case of very large lambda; bias is low, variance is high
C
In case of very large lambda; bias is high, variance is low
D
In case of very large lambda; bias is high, variance is high
Answer: In case of very large lambda; bias is high, variance is low
17
Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
A
In case of very small lambda; bias is low, variance is low
B
In case of very small lambda; bias is low, variance is high
C
In case of very small lambda; bias is high, variance is low
D
In case of very small lambda; bias is high, variance is high
19
Which of the following statement is true about partial derivative of the cost functions w.r.t weights / coefficients in linear-regression and logistic-regression?
