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Cross Validation False Negatives / NegativesPowerPoint Presentation

Cross Validation False Negatives / Negatives

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### Cross ValidationFalse Negatives / Negatives

ד"ר אבי רוזנפלד

False Positives / Negatives

Confusion matrix 1

Confusion matrix 2

FN

Actual

Actual

FP

Predicted

Predicted

Precision (P) = 20 / 50

Recall (P) = 20 / 30

עוד דוגמא עם כמה קטגוריות

27 animals — 8 cats, 6 dogs, and 13 rabbits

Confusion Matrix:

Predicted class

Actual class Cat Dog Rabbit

Cat 5 3 0

Dog 2 3 1

Rabbit 0 2 11

יש 3 False Negatives של חתולים שמסויגים כמו כלבים, ויש 2 False Positives של כלבים המסווגים כמו ארנבות.

ויש 2 False Positive של ארנבות שמסויגים ככלבים, ו0 False Positives של חתולים.

Recall (Dog) = 3/6, Precision(Dog) = 3/8

דוגמא מWEKA

=== Summary ===

Correctly Classified Instances 320 66.39 %

Incorrectly Classified Instances 162 33.61 %

=== Detailed Accuracy By Class ===

Precision Recall

0.664 1

0 0

=== Confusion Matrix ===

a b <-- classified as

320 0 | a = FALSE -> Precision (A) = 320/582), Recall = 320/320

162 0 | b = TRUE -> Precision (B) = Recall (B) = 0

דוגמא נוספת (מהחיות – (zoo.arff

Correctly Classified Instances 93 92.0792 %

Incorrectly Classified Instances 8 7.9208 %

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class

1 0 1 1 1 1 mammal

1 0 1 1 1 1 bird

0.6 0.01 0.75 0.6 0.667 0.793 reptile

1 0.011 0.929 1 0.963 0.994 fish

0.75 0 1 0.75 0.857 0.872 amphibian

0.625 0.032 0.625 0.625 0.625 0.92 insect

0.8 0.033 0.727 0.8 0.762 0.986 invertebrate

=== Confusion Matrix ===

a b c d e f g <-- classified as

41 0 0 0 0 0 0 | a = mammal

0 20 0 0 0 0 0 | b = bird

0 0 3 1 0 1 0 | c = reptile

0 0 0 13 0 0 0 | d = fish

0 0 1 0 3 0 0 | e = amphibian

0 0 0 0 0 5 3 | f = insect

0 0 0 0 0 2 8 | g = invertebrate

Recall (Invertebrate) = 8/10 = 0.8, Precision = 8/11 = 0.727

בעיית הOVERFITTING

10-fold cross-validation (one example of K-fold cross-validation)

- 1. Randomly divide your data into 10 pieces, 1 through k.
- 2. Treat the 1st tenth of the data as the test dataset. Fit the model to the other nine-tenths of the data (which are now the training data).
- 3. Apply the model to the test data (e.g., for logistic regression, calculate predicted probabilities of the test observations).
- 4. Repeat this procedure for all 10 tenths of the data.
- 5. Calculate statistics of model accuracy and fit (e.g., ROC curves) from the test data only.

תמונה cross-validation)

ניתוח התוצאות cross-validation)

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