Regression MCQs

1. What is the main goal of regression analysis?

a) To predict a categorical label
b) To predict a continuous value based on input variables
c) To group similar data points together
d) To identify associations between variables

Answer: b) To predict a continuous value based on input variables


2. Which of the following is a type of supervised learning algorithm used for regression?

a) K-means
b) Linear regression
c) DBSCAN
d) Apriori algorithm

Answer: b) Linear regression


3. What does the R-squared (R²) value represent in regression analysis?

a) The strength of the relationship between the features
b) The proportion of the variance in the dependent variable explained by the independent variables
c) The slope of the regression line
d) The correlation between the features

Answer: b) The proportion of the variance in the dependent variable explained by the independent variables


4. In simple linear regression, what is the equation of the model?

a) Y = mX + b
b) Y = β₀ + β₁X
c) Y = aX² + bX + c
d) Y = α + X

Answer: b) Y = β₀ + β₁X


5. Which of the following is a non-linear regression model?

a) Logistic regression
b) Polynomial regression
c) Linear regression
d) Ridge regression

Answer: b) Polynomial regression


6. What is multicollinearity in regression analysis?

a) When independent variables are highly correlated with each other
b) When the dependent variable has missing values
c) When the data contains outliers
d) When the dependent variable is binary

Answer: a) When independent variables are highly correlated with each other


7. What is regularization in the context of regression?

a) Reducing the number of data points in the dataset
b) Adding a penalty term to the cost function to avoid overfitting
c) Increasing the size of the dataset
d) Dividing the data into training and testing sets

Answer: b) Adding a penalty term to the cost function to avoid overfitting


8. Which of the following algorithms applies Lasso (L1) regularization in regression?

a) Ridge regression
b) Linear regression
c) Lasso regression
d) Decision tree regression

Answer: c) Lasso regression


9. What is the purpose of cross-validation in regression tasks?

a) To reduce the size of the dataset
b) To find the optimal values for the independent variables
c) To assess the model’s generalization ability and avoid overfitting
d) To improve the accuracy of the training data

Answer: c) To assess the model’s generalization ability and avoid overfitting


10. Which of the following methods is used to evaluate the performance of a regression model?

a) Confusion matrix
b) Mean Absolute Error (MAE)
c) Precision-Recall curve
d) F1-score

Answer: b) Mean Absolute Error (MAE)

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