Coursera Answers

Machine Learning With Python Week 2 Quiz Answer | Regression

Machine Learning With Python Week 2 Quiz Answer | Regression

Machine Learning With Python Week 2 Quiz Answer | Regression


Question 1)

Multiple Linear Regression is appropriate for:

  • Predicting the sales amount based on month
  • Predicting whether a drug is effective for a patient based on her characteristics
  • Predicting tomorrow’s rainfall amount based on the wind speed and temperature

Question 2)

Which of the following is the meaning of “Out of Sample Accuracy” in the context of evaluation of models?

  • “Out of Sample Accuracy” is the accuracy of an overly trained model (which may captured noise and produced a non-generalized model)
  • “Out of Sample Accuracy” is the percentage of correct predictions that the model makes on data that the model has NOT been trained on.

Question 3)

When should we use Multiple Linear Regression?

  • When there are multiple dependent variables
  • When we would like to predict impacts of changes in independent variables on a dependent variable.
  • When we would like to identify the strength of the effect that the independent variables have on a dependent variable.

Question 4)

Which sentence is NOT TRUE about Non-linear Regression?

  • Non-linear regression must have more than one dependent variable.
  • For a model to be considered non-linear, y must be a non-linear function of the parameters.
  • Nonlinear regression is a method to model non linear relationship between the dependent variable and a set of independent variables.

Question 5)

Which of the following statements are true about Polynomial regression?

  • Polynomial regression fits a curve line to your data.
  • Polynomial regression models can fit using the Least Squares method.
  • Polynomial regression can use the same mechanism as Multiple Linear Regression to find the parameters.