Machine Learning Regression: Linear Regression

What is the goal of linear regression?

• To analyze time series data

• To predict categorical outcomes

• To calculate the mean of the variables

• To model the relationship between variables using a linear equation.

Explain the difference between simple linear regression and multiple linear regression.

• Simple linear regression is more accurate than multiple linear regression.

• In simple linear regression, there are two or more independent variables, whereas in multiple linear regression, there is only one independent variable.

• In simple linear regression, there is only one independent variable, whereas in multiple linear regression, there are two or more independent variables.

• Simple linear regression is used for categorical data, while multiple linear regression is used for continuous data.

What is the formula for a simple linear regression model?

• y = ax + b

• y = mx - b

• y = cx + m

• y = mx + c

How is the best-fit line determined in linear regression?

• By minimizing the sum of squared differences between observed values and predicted values using the method of least squares.

• By flipping a coin

• By choosing the highest value

• By asking a magic eight ball

What is the coefficient of determination (R-squared) in linear regression?

• R-squared is a value between 0 and 100.

• R-squared is a value between -1 and 1.

• R-squared is a value between 0 and 1.

• R-squared is a value between 0 and 2.

What is the purpose of the cost function in linear regression?

• To calculate the accuracy of the model

• To measure the performance of the model by calculating the difference between predicted and actual values.

• To determine the number of features in the dataset

• To select the learning rate for gradient descent

What are the assumptions of linear regression?

• Linearity, Independence, Homoscedasticity, Skewness of residuals

• Linearity, Dependence, Heteroscedasticity, Normality of residuals

• Non-linearity, Independence, Homoscedasticity, Normality of residuals

• Linearity, Independence, Homoscedasticity, Normality of residuals

How is the error calculated in linear regression?

• By taking the absolute difference between predicted and actual values

• By dividing the sum of squared differences by the square root of the number of data points

• By multiplying the predicted values with actual values

• By taking the sum of squared differences between predicted and actual values divided by the number of data points.

What is the role of gradient descent in linear regression?

• Minimize the accuracy by updating model parameters iteratively.

• Minimize the cost function by updating model parameters randomly.

• Maximize the cost function by updating model parameters iteratively.

• Minimize the cost function by updating model parameters iteratively.

Explain the concept of regularization in linear regression.

• Regularization in linear regression has no impact on preventing overfitting

• Regularization in linear regression helps in controlling the complexity of the model by adding a penalty term to the cost function, which includes the magnitude of the coefficients. This penalty term helps in preventing overfitting and improving the model's ability to generalize to new data.

• Regularization in linear regression only works for categorical variables

• Regularization in linear regression involves increasing the complexity of the model by adding more features