Exploring Linear Models

What is the primary purpose of linear regression?

• To predict the value of a dependent variable based on the values of independent variables.

• To visualize data in a scatter plot.

• To determine the mean of a dataset.

• To analyze the correlation between two variables.

What is the equation of a simple linear regression model?

• y = mx + b

• y = mx^2 + c

• y = ax^2 + b

• y = m + bx

Name one assumption of linear regression models.

• Independence

• Homoscedasticity

• Normality

• Linearity

What is the difference between linear regression and linear classification?

• Linear regression is for predicting continuous values; linear classification is for predicting discrete classes.

• Linear regression is used for classification tasks; linear classification is for regression tasks.

• Linear regression predicts probabilities; linear classification predicts exact values.

• Linear regression requires categorical data; linear classification requires numerical data.

What is the purpose of the fit() method in Scikit-learn?

• To train a machine learning model on a dataset.

• To evaluate the performance of a model.

• To visualize the dataset in a graph.

• To preprocess the data before training.

How do you evaluate the performance of a linear regression model?

• Use R-squared, MAE, MSE, and RMSE.

• Evaluate based on the number of features used.

• Use only the coefficient values.

• Check the model's training time.

What does the term 'overfitting' mean in the context of linear models?

• Overfitting means the model is too simple, missing important patterns.

• Overfitting occurs when the model is perfectly accurate on training data but fails on test data due to simplicity.

• Overfitting refers to a model that generalizes well to new data, indicating robustness.

• Overfitting in linear models means the model is too complex, capturing noise instead of the true relationship.

Which Scikit-learn function is used to split data into training and testing sets?

• data_split

• train_test_split

• test_split

• train_split

How can you visualize the results of a linear regression model?

• Use pie charts to display data distribution

• Use scatter plots with regression lines, residual plots, and diagnostic plots.

• Utilize line graphs for time series analysis

• Create bar graphs for categorical comparison

What is the purpose of regularization in linear models?

• To increase the complexity of the model.

• The purpose of regularization in linear models is to prevent overfitting.

• To reduce the number of features in the model.

• To reduce the accuracy of predictions on training data.