Chap4_EvaluatingPerformance - Chapter 4 Evaluating...

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Chapter 4 – Evaluating Classification & Predictive Performance © Galit Shmueli and Peter Bruce 2008 Data Mining for Business Intelligence Shmueli, Patel & Bruce
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Why Evaluate? Multiple methods are available to classify or predict For each method, multiple choices are available for settings To choose best model, need to assess each model’s performance
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Accuracy Measures (Classification)
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Misclassification error Error = classifying a record as belonging to one class when it belongs to another class. Error rate = percent of misclassified records out of the total records in the validation data
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Naïve Rule Often used as benchmark: we hope to do better than that Exception: when goal is to identify high-value but rare outcomes, we may do well by doing worse than the naïve rule (see “lift” – later) Naïve rule: classify all records as belonging to the most prevalent class
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Separation of Records “High separation of records” means that using predictor variables attains low error “Low separation of records” means that using predictor variables does not improve much on naïve rule
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Confusion Matrix 201 1’s correctly classified as “1” 85 1’s incorrectly classified as “0” 25 0’s incorrectly classified as “1” 2689 0’s correctly classified as “0” Actual Class 1 0 1 201 85 0 25 2689 Predicted Class Classification Confusion Matrix
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Error Rate Overall error rate = (25+85)/3000 = 3.67% Accuracy = 1 – err = (201+2689) = 96.33% If multiple classes, error rate is: (sum of misclassified records)/(total records) Actual Class 1 0 1 201 85 0 25 2689 Predicted Class Classification Confusion Matrix
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Cutoff for classification Most DM algorithms classify via a 2-step process: For each record, 1. Compute probability of belonging to class “1” 2. Compare to cutoff value, and classify accordingly Default cutoff value is 0.50 If >= 0.50, classify as “1” If < 0.50, classify as “0” Can use different cutoff values Typically, error rate is lowest for cutoff = 0.50
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Cutoff Table Actual Class Prob. of "1" Actual Class Prob. of "1" 1 0.996 1 0.506 1 0.988 0 0.471 1 0.984 0 0.337 1 0.980 1 0.218 1 0.948 0 0.199 1 0.889 0 0.149 1 0.848 0 0.048 0 0.762 0 0.038 1 0.707 0 0.025 1 0.681 0 0.022 1 0.656 0 0.016 0 0.622 0 0.004 If cutoff is 0.50: eleven records are classified as “1” If cutoff is 0.80: seven records are classified as “1”
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Confusion Matrix for Different Cutoffs 0.25 Actual Class owner non-owner owner 11 1 non-owner 4 8 0.75 Actual Class owner non-owner owner 7 5 non-owner 1 11 Cut off Prob.Val. for Success (Updatable) Classification Confusion Matrix Predicted Class Cut off Prob.Val. for Success (Updatable) Classification Confusion Matrix Predicted Class
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Lift
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When One Class is More Important Tax fraud Credit default Response to promotional offer Detecting electronic network intrusion Predicting delayed flights In many cases it is more important to identify members of one class In such cases, we are willing to tolerate greater overall error, in return for better identifying the important class for further attention
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