Regression QUIZ (MCQ QUESTIONS AND ANSWERS)

10000 to 20000

10000 to 40000

25000 to 50000

10000 to 50000

1

The slope parameter

The intercept parameter

None

actual y‐coordinate value - predicted y‐coordinate value

predicted y‐coordinate value - actual y coordinate value

actual y‐coordinate value / predicted y‐coordinate value

None

The perpendicular distance between a data point and the regression line

The euclidian distance between a data point and the regression line

The horizontal distance between a data point and the regression line

The vertical distance between a data point and the regression line

43

53

23

None

2

1

-2

4

Simple Linear Regression

Multiple Linear Regression

Logistic Regression

All of the Above

Simple linear regression

Multiple linear regression

Polynomial regression

Logistic regression

Yes, we can

No, we can not

Partially we can

None

The data was not linear

The data has outliers

Both A or B depending on the context

None

Polynomial regression

Random forest regression

Simple linear regression

None

The model was over fitted with the training data

The model was under fitted with the training data

The model is absolutely fine

None

The average distance between all the data points

The distance between any two data points

The distance between two boundary data points

The minimum distance between any two data points

A function that calculates the distance of two boundary data points

A function that maps the value from one dimension to the other

A function that predicts the output value of a regression

None

Polynomial Kernel

Gaussian Kernel

Sigmoid Kernel

None

Boundary threshold

Error threshold

Distance threshold

None

Information entropy

Information gain

Both A and B

None

Logistic Regression

Support Vector Regression

Polynomial Regression

None

Simple linear regression

Decision tree

Random forest

All of the above

-5

5

2.5

-2.5

Linear Regression

Logistic Regression

Both A and B

None

Classification task

Regression task

Clustering task

None

Supervised Learning Algorithm

Unsupervised Learning Algorithm

Reinforcement Learning Algorithm

None

e equation itself choose the learning rate

Becomes slower with a large number of features

Iteration is not required

All of them

Logarithmic Loss

Area Under Curve

Both A and B

Least Square Error

Bias decreases and Variance increases

Bias increases and Variance increases

Bias increases and Variance decreases

Bias decreases and Variance decreases

The model will overfit the data

The model will underfit the data

The model will perform perfectly

None

AUC-ROC

Mean-Squared-Error

Accuracy

f1 score

sensitive to outliers

not sensitive to outliers

not affected by outliers

None

Higher is better

Lower is better

A or B depending on the context

None