Supervised Machine Learning: Regression | Coursera Quiz Answers

Supervised Machine Learning: Regression | Coursera (IBM)

This course serves as an introduction to Regression, one of the core modeling families in supervised Machine Learning. You will explore how to train regression models to predict continuous outcomes and learn to use error metrics to evaluate and compare different models. Additionally, the course covers best practices such as the application of train-test splits and various regularization techniques.

Upon completion, you will be able to distinguish between the uses and applications of classification and regression in supervised machine learning. You will also learn to describe and implement linear regression models, employ a range of error metrics to choose the most appropriate linear regression model for your data, and understand the role of regularization in preventing overfitting. The course will also cover regularization techniques including Ridge, LASSO, and Elastic Net.

This course is designed for aspiring data scientists who seek practical experience with Supervised Machine Learning Regression techniques in a business context. To benefit fully from the course, you should have experience with programming in a Python development environment and a basic understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

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Notice!
Always refer to the module on your course for the most accurate and up-to-date information.

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If you have any questions that are not covered in this post, please feel free to leave them in the comments section below. Thank you for your engagement.

WEEK 1 QUIZ

1. You can use supervised machine learning for all of the following examples, EXCEPT:

• Segment customers by their demographics.

2. The autocorrect on your phone is an example of:

• Supervised learning

3. Which of the following is the type of Machine Learning that uses only data with outcomes to build a model?

• Supervised Machine Learning

4. Which among the following options does not conform to the best practice of modelling in Supervised Machine learning?

• Develop multiple models.

5. This is the syntax you need to predict new data after you have trained a linear regression model called LR :

• LR.predict(X_test)

6. All of these options are useful error measures to compare regressions except:

• ROC index

7. All of the listed below are part of the Machine Learning Framework, except:

• Observations Features Parameters Answer: None of the above

8. Select the option that is the most INACCURATE regarding the definition of Machine Learning:

• Machine Learning is automated and requires no programming

9. In Linear Regression, which statement is correct about Sum Squared Error?

• The Sum Squared Error measures the distance between the truth and predicted values.

10. When learning about regression we saw the outcome as a continuous number. Given the below options what is an example of regression?

• Housing prices

WEEK 2 QUIZ

1. The main purpose of splitting your data into a training and test sets is:

• To avoid overfitting

2. Complete the following sentence: The training data is used to fit the model, while the test data is used to:

• Measure error and performance of the model

3. What term is used if your test data leaks into the training data?

• Data leakage

4. Which one of the below terms uses a linear combination of features?

• Linear Regression

5. When splitting your data, what is the purpose of the training data?

• Fit the actual model and learn the parameters

6. Polynomial features capture what effects?

• Non-linear effects.

7. Which fundamental problems are being solved by adding non-linear patterns, such as polynomial features, to a standard linear approach?

• Prediction and Interpretation.

8. A testing data could be also referred to as:

• Unseen data

9. Select the correct syntax to obtain the data split that will result in a train set that is 60% of the size of your available data:

• X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)

10. What is the correct sklearn syntax to add a third-degree polynomial to your model?

• polyFeat = PolynomialFeatures(degree=3)

WEEK 3 QUIZ

1. In K-fold cross-validation, how will increasing k affect the variance (across subsamples) of estimated model parameters?

• Increasing k will usually increase the variance of estimated model parameters.

2. Which statement about K-fold cross-validation below is TRUE?

• Each of the k subsamples in K-fold cross-validation is used as a test set

3. If a low-complexity model is underfitting during estimation, which of the following is MOST LIKELY true (holding the model constant) about K-fold cross-validation?

• K-fold cross-validation will still lead to underfitting, for any k

4. Which of the following statements about a high-complexity model in a linear regression setting is TRUE?
A high variance of parameter estimates across cross-validation subsamples indicates likely overfitting.
5. Reviewing the below graph, what is the model considered when associated with the left side of this curve before hitting the plateau?

• Underfitting

6. Reviewing the below graph, what is the model considered when associated with the right side of the cross-validation error?

• Overfitting

7. Which of the following functions perform K-fold cross-validation for us, appropriately fitting and transforming at every step of the way?

• 'cross_val_predict'

8. Which of the following statements about cross-validation is/are True?

• Cross-validation is an essential step in hyperparameter tuning.
• We can manually generate folds by using the KFold function.
• ANSWER - ALL THE ABOVE

9. Which of the following statements about GridSearchCV is/are True?

• GridSearchCV scans over a dictionary of parameters.
• GridSearchCV finds the hyperparameter set that has the best out-of-sample score.
• GridSearchCV retrains on all data with the "best" hyperparameters.
• ANSWER - ALL THE ABOVE

10. Which of the below functions randomly selects data to be in the train/test folds?

• KFold and StratifiedKFold

WEEK 4 QUIZ

1. Which of the following statements about model complexity is TRUE?

• Higher model complexity leads to a higher chance of overfitting.

2.Which of the following statements about model errors is TRUE?

• Underfitting is characterized by higher errors in both training and test samples.

3. Which of the following statements about regularization is TRUE?

• Regularization decreases the likelihood of overfitting relative to training data.

4. Which of the following statements about scaling features prior to regularization is TRUE?

• The larger a feature’s scale, the more likely its estimated impact will be influenced by regularization.

5. Which one of the 3 Regularization techniques: Ridge, Lasso, and Elastic Net, performs the fastest under the hood?

• Ridge

6. Which of the following statements about Elastic Net regression is TRUE?

• Elastic Net combines L1 and L2 regularization.

7. BOTH Ridge regression and Lasso regression

• Add a term to the loss function proportional to a regularization parameter.

8. Compared with Lasso regression (assuming similar implementation), Ridge regression is:

• Less likely to set feature coefficients to zero.

9. Which of the following about Ridge Regularization is TRUE?

• It enforces the coefficients to be lower, but not 0
• It minimizes irrelevant features
• It penalizes the size magnitude of the regression coefficients by adding a squared term
• ANSWER: All of the above

10. Whixh of the below statements are correct?

• Only LassoCV use L1 regularization function.

WEEK 5 QUIZ

1. When working with regularization, what is the view that illuminates the actual optimization problem and shows why LASSO generally zeros out coefficients?

• Answer: Geometric view

2. When working with regularization, what is the view that recalibrates our understanding of LASSO and Ridge, as a base problem, where coefficients have particular prior distributions?

• Answer: Probabilistic view

3. When working with regularization, what is the logical view of how to achieve the goal of reducing complexity?

• Answer: Analytical view

4. All of the following statements about Regularization are TRUE except:

• Answer: Features should rarely or never be scaled prior to implementing regularization.

5. When working with regularization and using the geometric formulation, what is found at the intersection of the penalty boundary and a contour of the traditional OLS cost function surface?

• Answer: The cost function minimum

6. Which statement under the Probabilistic View is correct?

• Answer: Regularization imposes certain priors on the regression coefficients.

7. Increasing L2/L1 penalties force coefficients to be smaller, restricting their plausible range. This statement is part of what View?

• Answer: Analytic View

8. What does a higher lambda term mean in Regularization technique?

• Answer: Higher lambda decreases variance, means smaller coefficients.

9. What concept/s under Probabilistic View is/are True?

• Answer: All of the above (We can derive the posterior probability by knowing the probability of the target and the prior distribution, The prior distribution is derived from independent draws of a prior coefficient density function that we choose when regularizing, L2 (ridge) regularization imposes a Gaussian prior on the coefficients, while L1 (lasso) regularization imposes a Laplacian prior.)

10. What statement is True?

• Answer: We reduce the complexity of the model by minimizing the error on our training set.

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