machine learning with python ibm coursera quiz answers week 2
Practice Quiz: Regression
1. 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 capture noise and produced a non-generalized model)
- “Out of Sample Accuracy” is the accuracy of a model on all the data available.
- “Out of Sample Accuracy” is the percentage of correct predictions that the model makes using the test dataset.
- “Out of Sample Accuracy” is the percentage of correct predictions that the model makes on data that the model has NOT been trained on.
2. When should we use Multiple Linear Regression? (Select two)
- When we would like to examine the relationship between multiple variables.
- When we would like to predict impacts of changes in independent variables on a dependent variable.
- When there are multiple dependent variables
- When we would like to identify the strength of the effect that the independent variables have on a dependent variable.
3. Which sentence is TRUE about linear regression?
- A linear relationship is necessary between the independent and dependent variables as well as in between independent variables.
- A linear relationship is necessary between the independent variables and the dependent variable.
- Simple linear regression requires a linear relationship between the predictor and the response, but multiple linear regression does not.
- Multiple linear regression requires a linear relationship between the predictors and the response, but simple linear regression does not.
Graded Quiz: Regression
4. What are the requirements for independent and dependent variables in regression?
- Independent variables can be either categorical or continuous. Dependent variables must be continuous.
- Independent variables must be continuous. Dependent variables can be either categorical or continuous.
- Independent and dependent variables can be either categorical or continuous.
- Independent and dependent variables must be continuous.
5. The key difference between simple and multiple regression is:
- To estimate a single dependent variable, simple regression uses one independent variable whereas multiple regression uses multiple.
- Simple linear regression compresses multidimensional space into one dimension.
- Simple regression assumes a linear relationship between variables, whereas this assumption is not necessary for multiple regression.
- Multiple linear regression introduces polynomial features.
6. Recall that we tried to predict CO2 emission with car information. Say that now we can describe the relationship as: CO2_emission = 130 - 2.4*cylinders + 8.3*fuel_consumption
What is TRUE of this relationship?
- When “cylinders” decreases by 1 while fuel_consumption remains constant, CO2_emission increases by 2.4 units.
- When “cylinders” increases by 1 while fuel_consumption remains constant, CO2_emission increases by 2.4 units.
- Since the coefficient for “fuel_consumption” is greater than that for “cylinders”, “fuel_consumption” has higher impact on CO2_emission.
- When both “cylinders” and “fuel_consumption” increase by 1 unit, CO2_emission decreases.
7. What could be the cause of a model yielding high training accuracy and low out-of-sample accuracy?
- The model is training on the entire dataset, so it is underfitting.
- The model is training on the entire dataset, so it is overfitting.
- The model is training on a small training set, so it is underfitting.
- The model is training on a small training set, so it is overfitting.
8. Multiple Linear Regression is appropriate for:
- Predicting the sales amount based on month.
- Predicting tomorrow’s rainfall amount based on the wind speed and temperature.
- Predicting whether a drug is effective for a patient based on her characteristics.
