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הערכת טיב המודל F-Measure, Kappa, Costs, MetaCost

הערכת טיב המודל F-Measure, Kappa, Costs, MetaCost. ד"ר אבי רוזנפלד. הגדרות. False Positives / Negatives. Confusion matrix 1. Confusion matrix 2. FN. Actual. Actual. FP. Predicted. Predicted. Precision (P) = 20 / 50 = 0.4 Recall (P) = 20 / 30 = 0.666 F-measure=2*.4*.666/1.0666=.5.

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הערכת טיב המודל F-Measure, Kappa, Costs, MetaCost

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  1. הערכת טיב המודלF-Measure, Kappa, Costs, MetaCost ד"ר אבי רוזנפלד

  2. הגדרות

  3. False Positives / Negatives Confusion matrix 1 Confusion matrix 2 FN Actual Actual FP Predicted Predicted Precision (P) = 20 / 50 = 0.4 Recall (P) = 20 / 30 = 0.666 F-measure=2*.4*.666/1.0666=.5

  4. The Kappa Statistic • Kappa measures relative improvement over random prediction • Dreal/ Dperfect = A (accuracy of the real model) • Drandom / Dperfect= C (accuracy of a random model) • Kappa Statistic = (A-C) / (1-C) = (Dreal/ Dperfect – Drandom/ Dperfect ) / (1 – Drandom/ Dperfect ) Remove Dperfect from all places • (Dreal – Drandom) / (Dperfect – Drandom) • Kappa = 1 when A = 1 • Kappa  0 if prediction is no better than random guessing

  5. Aside: the Kappa statistic • Two confusion matrix for a 3-class problem: real model (left) vs random model (right) • Number of successes: sum of values in diagonal (D) • Kappa = (Dreal – Drandom) / (Dperfect – Drandom) • (140 – 82) / (200 – 82) = 0.492 • Accuracy = 140/200 = 0.70 Predicted Predicted total total Actual Actual total total

  6. The kappa statistic – how to calculate Drandom? Expected confusion matrix, E, for a random model Actual confusion matrix, C total total Actual Actual total total 100*120/200 = 60 Rationale: 100 actual values, 120/200 in the predicted class, so random is: 100*120/200

  7. Cost-Sensitive Learning • מוסיפים עלות לכל סוג של סיווג • בדרך כלל, אלגוריתמים לא מפרידים בין קטגוריות • אבל יש ה-ר-ב-ה יישומים שזה חשוב: • אבחון סרטן • אבחון סיכונים (התקפות מחשב, זיוף, וכו')

  8. Class Imbalance vs. Asymmetric Misclassification costs • Class Imbalance: one class occurs much more often than the other • Asymmetric misclassification costs: the cost of misclassifying an example from one class is much larger than the cost of misclassifying an example from the other class. • לפי הניסיון שלי: שני המוסגים בדרך כלל באים ביחד • גם הפתרונות דומות • דוגמאות: • תוריד שורות מהMAJORITY CLASS • תוסיף שורות לMINORITY CLASS • תוסיף עלות לMINORITY -- METACOST

  9. Making Classifier Balanced • Baseline Methods • Random over-sampling • Random under-sampling • Under-sampling Methods • Tomek links • Condensed Nearest Neighbor Rule • One-sided selection • CNN + Tomek links • Neighborhood Cleaning Rule • Over-sampling Methods • Smote • Combination of Over-sampling method with Under-sampling method • Smote + Tomek links • Smote + ENN

  10. איזה מצב יותר טוב??? Confusion matrix 2 Confusion matrix 1 FN FN Actual Actual FP Predicted Predicted Cost matrix Error rate: 40/150 Cost: 30x1+10x2=50 Error rate: 35/150 Cost: 15x1+20x2=55

  11. MetaCost • By wrapping a cost-minimizing procedure, “meta-learning” stage, around the classifier • תתייחס לסוג הלמידה כ"קופצה שחורה" • אפשר לשנות את הCOST ולהשפיע על התוצאות

  12. דוגמא:מתי אנשים מפעילים CRUISE CONTROL

  13. מתוך WEKAMetacostבלי תוספת עלות

  14. מתוך WEKAMetacostעם עלות

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