Linear Regression in Python – Quiz 1

What is the main purpose of linear regression?

• To classify data into categories

• To cluster data into groups

• To find a linear relationship between the features and the dependent variable

• To reduce the dimensionality of data

What does the .fit() method do in LinearRegression()?

• Plots the regression line

• Evaluates the model accuracy

• Trains the model on the given data

• Predicts values using the trained model

Which function is used to make predictions once the model is trained?

• model.evaluate()

• model.transform()

• model.predict()

• model.prediction()

How do you access the coefficient (slope) of the regression line in a trained model?

• model.bias_

• model.weights_

• model.coef_

• model.slope_

What does the model.intercept_ attribute represent?

• The root mean square error

• The slope of the regression line

• The predicted value

• The y-intercept of the regression line

What metric is commonly used to evaluate the performance of a linear regression model?

• Confusion Matrix

• R-squared (R²) Score

• F1 Score

• Recall

Which of the following is a correct way to visualize a simple linear regression result?

• sns.heatmap()

• plt.scatter() + plt.plot()

• plt.hist()

• sns.pairplot()

What will happen if the input features are not reshaped before fitting in scikit-learn (e.g., 1D array)?

• Nothing, it will work

• A warning will be shown

• An error will occur because X must be 2D

• The model will ignore the shape