Practical Data Science using Python - Regression Models and Performance Metrics

The video tutorial covers regression predictive modeling, focusing on linear regression and its application in predicting continuous variables like house prices. It explains the importance of minimizing the differences between actual and predicted values using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The tutorial also discusses the gradient descent algorithm for error minimization and introduces other metrics like residual sum of squares and the coefficient of determination (R2) for evaluating regression models.

What type of regression problem involves multiple input variables?

• Discrete regression

• Time series forecasting

• Multivariate regression

• Univariate regression

Which algorithm is commonly used in linear regression to minimize prediction errors?

• Support Vector Machine

• K-Means Clustering

• Random Forest

• Gradient Descent

What is the primary goal of a regression learning algorithm?

• Maximize the differences between actual and predicted values

• Minimize the differences between actual and predicted values

• Increase the number of input variables

• Decrease the number of output variables

Which metric is known for its magnifying effect on larger differences due to squaring?

• Mean Squared Error

• Total Sum of Squares

• Residual Sum of Squares

• Mean Absolute Error

How is the Root Mean Squared Error (RMSE) related to the Mean Squared Error (MSE)?

• RMSE is half the MSE

• RMSE is the square of MSE

• RMSE is the square root of MSE

• RMSE is double the MSE

What is the effect of squaring differences in the calculation of MSE?

• It magnifies the impact of large errors

• It reduces the impact of large errors

• It averages the errors

• It has no effect on the errors

What does the Mean Absolute Error (MAE) measure in regression analysis?

• The absolute differences between actual and predicted values

• The proportion of variance explained by the model

• The squared differences between actual and predicted values

• The total sum of squares of residuals

Which metric is calculated as the sum of squared residuals divided by the total sum of squares?

• Mean Absolute Error

• Mean Squared Error

• Root Mean Squared Error

• Coefficient of Determination (R2)

What is the purpose of using the Coefficient of Determination (R2) in regression?

• To measure the total error in predictions

• To determine the number of input variables

• To assess the proportion of variance explained by the model

• To calculate the mean of residuals

Which error metric is similar to MSE but uses absolute differences instead of squared differences?

• Mean Absolute Error

• Residual Sum of Squares

• Total Sum of Squares

• Coefficient of Determination