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Machine Learning Classification & Regression Trees

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Minitab, LLC
Minitab, LLC

In this final segment of our 3-part series, we will focus on classification and regression trees, or CART®. There are many applications for CART, such as determining the conditions that lead to process defects, predicting the probability of fraudulent transactions and determining the key drivers of anomalies in manufacturing processes.

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Machine Learning with Classification & Regression Trees
© 2020 Minitab, LLC. • 25+ years of experience • Minitab Trainer • Statistical Consultant • Minitab Software Designer • Master's in Statistics Meet the Presenter: Cheryl Pammer Senior Advisory Statistician
© 2020 Minitab, LLC. Learning Objectives 4 ►Overview of Classification and Regression Trees ►Model Selection ►Visualizing Information from Large Trees ►Prediction
© 2020 Minitab, LLC. Basic Supervised Machine Learning Algorithms ►Continuous Y: Regression, CART Regression Trees ►Categorical Y: Logistic Regression, CART Classification Trees 5
© 2020 Minitab, LLC. Example: Pulp Bleaching ►A paper manufacturer needs to determine which predictors contribute to defects in a pulp bleaching process. ►Several months of observational process data are mined to determine the root cause of defects. 6
© 2020 Minitab, LLC. Logistic Regression 7 ?
© 2020 Minitab, LLC. Challenges with Regression Regression and logistic regression often don’t work well with larger observational data sets. ►Everything is significant. ►Assumptions are not met. ►Relationships are nonlinear. ►Local interactions exist. ►Extreme outliers. ►Many missing values. 8
© 2020 Minitab, LLC. Classification and Regression Trees (CART) Find X’s that best partition the data into similar groupings of Y. ►CART Classification Tree  Partitions data into high percentage of Group A and high percentage of Group B  Example: Separate data into defective and non-defective groupings ►CART Regression Tree  Partitions data into high values of Y and low values of Y  Example: Find conditions that lead to optimal values of Y 9
© 2020 Minitab, LLC. CART Classification Tree 10
© 2020 Minitab, LLC. CART Classification Tree 11
© 2020 Minitab, LLC. CART Classification Tree ►Will continue to split data into groupings to separate defects from nondefects. ►Continues splitting until:  Runs out of data  Hits a stopping rule such as terminal nodes need at least 3 observations. 12
© 2020 Minitab, LLC. Model Selection ►Simpler models (fewest nodes) may be adequate. ►Look for elbow in plot. 13
© 2020 Minitab, LLC. 10-Node Tree ►Simpler tree is easier to interpret. ►Less tendency toward over-fitting. ►Will be more misclassifications. ►Here, most defect hotspots involve just 2 variables. 14
© 2020 Minitab, LLC. Receiver Operating Characteristic (ROC) Curve ►Plot of True Positive Rate vs False Positive Rate ►For a random classifier True Positive Rate = False Positive Rate 15 Sensitivity = TP/(TP + FN) Specificity = TN/(FP + TN) Model = Fail Model = Pass True Fail #TP #FN True Pass #FP #TN
© 2020 Minitab, LLC. Confusion Matrix 16 Model = Fail Model = Pass True Fail #TP #FN True Pass #FP #TN Sensitivity = TP/(TP + FN) Specificity = TN/(FP + TN)
© 2020 Minitab, LLC. Variable Importance 17
© 2020 Minitab, LLC. Hot Spots ►Overall defect rate = 2.9% ►Four nodes had defect rates > 15% 18
© 2020 Minitab, LLC. Example: Glass Manufacturing Process A glass bottle manufacturer wants to use past data to: ►Determine what increases per unit cost of manufacture. ►Predict the per unit cost of manufacture of custom orders to determine pricing. 19
© 2020 Minitab, LLC. Relationships Between Continuous Variables 20
© 2020 Minitab, LLC. Counts by Plant, Segment and Color 21
© 2020 Minitab, LLC. CART Regression Tree ►Find predictor and split point that best separates the cases into two groups with similar response values. ►Splitting criterion is usually least squared error. For this method, predictions (fits) are mean of data in each terminal node. 22
© 2020 Minitab, LLC. CART Regression Tree (Least Squares Loss) ►For each predictor: 1. Sort smallest to largest 2. Calculate split improvement for each possible split point 3. Select best split point for variable 4. Compare best split improvement across all variables ►For continuous predictor, split point is midpoint between distinct values ►For categorical predictor, split point is a set of values 23
© 2020 Minitab, LLC. CART Regression Tree Example 1. Sort by X1 24
© 2020 Minitab, LLC. CART Regression Tree Example 2. Split at each possible split point and calculate split improvement. 25
© 2020 Minitab, LLC. CART Regression Tree Example 3. Select the best split point for variable. 4. Compare best split improvement across all variables. 26
© 2020 Minitab, LLC. CART Regression Tree Example 27
© 2020 Minitab, LLC. CART Regression Tree 28
© 2020 Minitab, LLC. CART Regression Tree ►Even with small number of predictors, optimal tree can be large. ►Interpret the tree using visualizations based on stored fits and terminal node information. 29
© 2020 Minitab, LLC. Model Selection “Optimal” model within 1 SE of highest Test R2. 30
© 2020 Minitab, LLC. Using CART for Prediction (Fit) ►Classification tree: Can use either predicted class or predicted probability of terminal node. ►Regression tree: Fit is mean or median of terminal node, depending on estimation method. 31
© 2020 Minitab, LLC. Diagnostics 32
© 2020 Minitab, LLC. Exploring the CART Results ►Store the Fits and Terminal Node Ids when creating the model. ►Use these stored values and Minitab’s visualization tools to explore results. 33
© 2020 Minitab, LLC. Terminal Node Sample Sizes 34
© 2020 Minitab, LLC. Variable Importance 35
© 2020 Minitab, LLC. Predicted Cost by Segment 36
© 2020 Minitab, LLC. Highest Predicted Cost 37 Brushing Mode Add Variables and Make Indicator Variable
© 2020 Minitab, LLC. Terminal Node 71 vs Other Nodes 38
© 2020 Minitab, LLC. Terminal Node 71 Predictor Demographics 39
© 2020 Minitab, LLC. What is the Predicted Manufacturing Cost? 40
© 2020 Minitab, LLC. Take-aways ►Overview of Classification and Regression Trees ►Model Selection ►Visualizing Information from Large Trees ►Prediction Questions! Cheryl Pammer cpammer@minitab.com 41
© 2020 Minitab, LLC. Upcoming Webinars and Virtual Events • Maximize Efficiency: Using Minitab Workspace and Minitab Statistical Software to Amplify Output and Meet Critical Demand Time: Wednesday 22 July, 12PM AEST (10AM HKT / 2PM NZST) • Turning Words in to Numbers and Numbers into Data-Driven Insights with Minitab, Python and Text Mining Time: Wednesday 29 July, 12PM AEST (10AM HKT / 2PM NZST) See all the details and sign up at: https://info.minitab.com/resources/webinars/webinar-wednesdays-apac
© 2020 Minitab, LLC. Upcoming Webinars and Virtual Events • Online/Virtual Training Minitab is now offering virtual training taught by Minitab experts – perfect for remote/home workers. Visit www.minitab.com/training/training for more info. • Talk to Minitab Complimentary resources to help you deal quickly with today's challenges and changing environment. Visit www.minitab.com and click on the Talk to Minitab button and a Minitab representative will be in touch!
© 2020 Minitab, LLC. Our Approach: More Than Business Analytics… Solutions Analytics Software Services Training Learn first-hand by attending public or customized trainings in your facilities according to your requirements. Statistical Consulting Personalized help with statistical challenges from collecting the right data to interpreting analysis more. Support Assistance with installation, implementation, version updates and license management. Master statistics and Minitab anywhere with online training Machine learning and predictive analytics software Start, track, manage and execute improvement projects with real-time dashboards Powerful statistical software everyone can use. Data Analysis Predictive Modeling Visual Business Tools Project Oversight Visual tools to process and product excellence. Online Training Solutions analytics is our integrated approach to providing software and services that enable organizations to make better decisions that drive business excellence.

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Machine Learning Classification & Regression Trees

  • 2. © 2020 Minitab, LLC. • 25+ years of experience • Minitab Trainer • Statistical Consultant • Minitab Software Designer • Master's in Statistics Meet the Presenter: Cheryl Pammer Senior Advisory Statistician
  • 3. © 2020 Minitab, LLC. Learning Objectives 4 ►Overview of Classification and Regression Trees ►Model Selection ►Visualizing Information from Large Trees ►Prediction
  • 4. © 2020 Minitab, LLC. Basic Supervised Machine Learning Algorithms ►Continuous Y: Regression, CART Regression Trees ►Categorical Y: Logistic Regression, CART Classification Trees 5
  • 5. © 2020 Minitab, LLC. Example: Pulp Bleaching ►A paper manufacturer needs to determine which predictors contribute to defects in a pulp bleaching process. ►Several months of observational process data are mined to determine the root cause of defects. 6
  • 6. © 2020 Minitab, LLC. Logistic Regression 7 ?
  • 7. © 2020 Minitab, LLC. Challenges with Regression Regression and logistic regression often don’t work well with larger observational data sets. ►Everything is significant. ►Assumptions are not met. ►Relationships are nonlinear. ►Local interactions exist. ►Extreme outliers. ►Many missing values. 8
  • 8. © 2020 Minitab, LLC. Classification and Regression Trees (CART) Find X’s that best partition the data into similar groupings of Y. ►CART Classification Tree  Partitions data into high percentage of Group A and high percentage of Group B  Example: Separate data into defective and non-defective groupings ►CART Regression Tree  Partitions data into high values of Y and low values of Y  Example: Find conditions that lead to optimal values of Y 9
  • 9. © 2020 Minitab, LLC. CART Classification Tree 10
  • 10. © 2020 Minitab, LLC. CART Classification Tree 11
  • 11. © 2020 Minitab, LLC. CART Classification Tree ►Will continue to split data into groupings to separate defects from nondefects. ►Continues splitting until:  Runs out of data  Hits a stopping rule such as terminal nodes need at least 3 observations. 12
  • 12. © 2020 Minitab, LLC. Model Selection ►Simpler models (fewest nodes) may be adequate. ►Look for elbow in plot. 13
  • 13. © 2020 Minitab, LLC. 10-Node Tree ►Simpler tree is easier to interpret. ►Less tendency toward over-fitting. ►Will be more misclassifications. ►Here, most defect hotspots involve just 2 variables. 14
  • 14. © 2020 Minitab, LLC. Receiver Operating Characteristic (ROC) Curve ►Plot of True Positive Rate vs False Positive Rate ►For a random classifier True Positive Rate = False Positive Rate 15 Sensitivity = TP/(TP + FN) Specificity = TN/(FP + TN) Model = Fail Model = Pass True Fail #TP #FN True Pass #FP #TN
  • 15. © 2020 Minitab, LLC. Confusion Matrix 16 Model = Fail Model = Pass True Fail #TP #FN True Pass #FP #TN Sensitivity = TP/(TP + FN) Specificity = TN/(FP + TN)
  • 16. © 2020 Minitab, LLC. Variable Importance 17
  • 17. © 2020 Minitab, LLC. Hot Spots ►Overall defect rate = 2.9% ►Four nodes had defect rates > 15% 18
  • 18. © 2020 Minitab, LLC. Example: Glass Manufacturing Process A glass bottle manufacturer wants to use past data to: ►Determine what increases per unit cost of manufacture. ►Predict the per unit cost of manufacture of custom orders to determine pricing. 19
  • 19. © 2020 Minitab, LLC. Relationships Between Continuous Variables 20
  • 20. © 2020 Minitab, LLC. Counts by Plant, Segment and Color 21
  • 21. © 2020 Minitab, LLC. CART Regression Tree ►Find predictor and split point that best separates the cases into two groups with similar response values. ►Splitting criterion is usually least squared error. For this method, predictions (fits) are mean of data in each terminal node. 22
  • 22. © 2020 Minitab, LLC. CART Regression Tree (Least Squares Loss) ►For each predictor: 1. Sort smallest to largest 2. Calculate split improvement for each possible split point 3. Select best split point for variable 4. Compare best split improvement across all variables ►For continuous predictor, split point is midpoint between distinct values ►For categorical predictor, split point is a set of values 23
  • 23. © 2020 Minitab, LLC. CART Regression Tree Example 1. Sort by X1 24
  • 24. © 2020 Minitab, LLC. CART Regression Tree Example 2. Split at each possible split point and calculate split improvement. 25
  • 25. © 2020 Minitab, LLC. CART Regression Tree Example 3. Select the best split point for variable. 4. Compare best split improvement across all variables. 26
  • 26. © 2020 Minitab, LLC. CART Regression Tree Example 27
  • 27. © 2020 Minitab, LLC. CART Regression Tree 28
  • 28. © 2020 Minitab, LLC. CART Regression Tree ►Even with small number of predictors, optimal tree can be large. ►Interpret the tree using visualizations based on stored fits and terminal node information. 29
  • 29. © 2020 Minitab, LLC. Model Selection “Optimal” model within 1 SE of highest Test R2. 30
  • 30. © 2020 Minitab, LLC. Using CART for Prediction (Fit) ►Classification tree: Can use either predicted class or predicted probability of terminal node. ►Regression tree: Fit is mean or median of terminal node, depending on estimation method. 31
  • 31. © 2020 Minitab, LLC. Diagnostics 32
  • 32. © 2020 Minitab, LLC. Exploring the CART Results ►Store the Fits and Terminal Node Ids when creating the model. ►Use these stored values and Minitab’s visualization tools to explore results. 33
  • 33. © 2020 Minitab, LLC. Terminal Node Sample Sizes 34
  • 34. © 2020 Minitab, LLC. Variable Importance 35
  • 35. © 2020 Minitab, LLC. Predicted Cost by Segment 36
  • 36. © 2020 Minitab, LLC. Highest Predicted Cost 37 Brushing Mode Add Variables and Make Indicator Variable
  • 37. © 2020 Minitab, LLC. Terminal Node 71 vs Other Nodes 38
  • 38. © 2020 Minitab, LLC. Terminal Node 71 Predictor Demographics 39
  • 39. © 2020 Minitab, LLC. What is the Predicted Manufacturing Cost? 40
  • 40. © 2020 Minitab, LLC. Take-aways ►Overview of Classification and Regression Trees ►Model Selection ►Visualizing Information from Large Trees ►Prediction Questions! Cheryl Pammer cpammer@minitab.com 41
  • 41. © 2020 Minitab, LLC. Upcoming Webinars and Virtual Events • Maximize Efficiency: Using Minitab Workspace and Minitab Statistical Software to Amplify Output and Meet Critical Demand Time: Wednesday 22 July, 12PM AEST (10AM HKT / 2PM NZST) • Turning Words in to Numbers and Numbers into Data-Driven Insights with Minitab, Python and Text Mining Time: Wednesday 29 July, 12PM AEST (10AM HKT / 2PM NZST) See all the details and sign up at: https://info.minitab.com/resources/webinars/webinar-wednesdays-apac
  • 42. © 2020 Minitab, LLC. Upcoming Webinars and Virtual Events • Online/Virtual Training Minitab is now offering virtual training taught by Minitab experts – perfect for remote/home workers. Visit www.minitab.com/training/training for more info. • Talk to Minitab Complimentary resources to help you deal quickly with today's challenges and changing environment. Visit www.minitab.com and click on the Talk to Minitab button and a Minitab representative will be in touch!
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