Linear Regression MCQ

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Logistic and linear both are regression algorithms. Which of the following are/is the reason(s) for not using linear regression as a classification algorithm?

In linear regression, which of the following are clear signs of overfitting?

What is the sensitivity of the following model?

Actual/Predicted	Not Churn	Churn
Not Churn	400	100
Churn	50	150

What is the number of False Positives for the model given below?

Actual/Predicted	Not Churn	Churn
Not Churn	400	100
Churn	50	150

Given the following confusion matrix calculate the accuracy of the model.

Matrix

Actual/Predicted	Nos	Yeses
Nos	1000	50
Yeses	250	1200

For the sigmoid curve ( β0=-15 and β1 =0.065) what will be the probability of diabetes for a patient with sugar level 240?

For the sigmoid curve ( β0=-15 and β1 =0.065), what will be the probability of diabetes for a patient with sugar level 240?

This is the Sigmoid Curve Equation: y = p ( d i a b e t e s ) = 1 1 + e−(β0+β1x)

Problem: This is the sigmoid curve equation: y = p ( d i a b e t e s ) = 1 1 + e − ( β 0 + β 1 x )

y = P (Diabetes) = $\frac{1}{1+e^{-(\beta 0-\beta 1 x)}}$

Here, let’s say you take β0 = -15 and β1 = 0.065. Now, what will be the probability of diabetes for a patient with sugar level 220?

Which of the following is true in case of projection? (More than one option may be correct.)

In which of the following cases can linear regression be used?

In the r-squared adjusted metric r-squared is “adjusted” or modified according to:

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