8.9: Exercises
- Page ID
- 139294
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Exercises: Applying Random Forests Concepts
The following exercises are designed to reinforce key learnings from Sections 8.1 through 8.7. These activities encourage students to apply their conceptual understanding of Random Forests in business contexts and reflect on their practical implications. An optional hands-on coding exercise is included for students who wish to explore Python implementation further.
1. Explain in your own words what a Random Forest is and how it differs from a single decision tree.
2. Identify and describe two real-world business scenarios where using a Random Forest model could improve decision-making.
3. List at least three benefits and two limitations of Random Forests in the context of business analytics.
4. Describe how the 'randomness' in Random Forests contributes to model robustness and accuracy.
5. Given a list of variables (e.g., Age, Income, Purchase History), explain how feature importance might help prioritize decision-making.
6. Analyze a sample confusion matrix and describe what the numbers represent in terms of model accuracy and errors.
7. Review a classification report and explain the significance of precision, recall, and F1-score in evaluating model performance.
8. Describe how Random Forest models can support customer churn prediction or credit scoring, and what variables might be relevant.
9. Create a simple visual to explain how multiple trees contribute to a Random Forest decision.
10. Summarize the key takeaways from the case study in Section 8.7 and how they apply to other business domains.
Optional Exercise:
Using Python, build a Random Forest model with a small dataset below. Display the confusion matrix and classification report. Interpret the results in plain language.
In the following dataset, each row represents a customer with various characteristics.
|
Age |
Income |
Tenure |
Location_Score |
Churned |
|---|---|---|---|---|
|
25 |
50000 |
2 |
3.5 |
0 |
|
45 |
75000 |
10 |
4.2 |
1 |
|
30 |
60000 |
3 |
3.0 |
0 |
|
50 |
82000 |
12 |
4.5 |
1 |
|
28 |
54000 |
4 |
2.9 |
0 |
|
39 |
70000 |
8 |
3.8 |
1 |
|
23 |
48000 |
1 |
2.7 |
0 |
|
60 |
95000 |
15 |
4.8 |
1 |
|
33 |
62000 |
5 |
3.1 |
0 |
|
41 |
72000 |
9 |
4.0 |
1 |
Feature Variables:
- Age: The customer’s age in years
- Income: Annual income in US dollars
- Tenure: Number of years the customer has been with the company
- Location_Score: A score representing proximity to company services (higher is better)
Target Variable:
- Churned: Whether the customer left the company (1) or stayed (0)
Goal:
The goal of this exercise is to train a Random Forest classifier on this dataset to predict whether a customer will churn. Students should use the training and prediction steps to examine how well the model classifies customers, analyze feature importance, and interpret the confusion matrix and classification report.