## Questions and Answers

What is the perfect score for recall?

What is the relationship between precision and recall?

What is the calculation for precision in the given example?

What is the purpose of the holdout method in classifier evaluation?

Signup and view all the answers

What is the main difference between cross-validation and stratified cross-validation?

Signup and view all the answers

What is the purpose of leave-one-out cross-validation?

Signup and view all the answers

What is the primary function of a classifier in data mining?

Signup and view all the answers

What is the formula to calculate the coverage of a rule?

Signup and view all the answers

What is the foundation of Naïve Bayes classification?

Signup and view all the answers

What is the characteristic of Naïve Bayes classification that allows it to incorporate prior knowledge with observed data?

Signup and view all the answers

What is the purpose of Bayes’ Theorem in classification?

Signup and view all the answers

What is the advantage of using Naïve Bayes classification?

Signup and view all the answers

What does P(H) represent in Bayes' Theorem?

Signup and view all the answers

What is the purpose of the validation test set in model selection?

Signup and view all the answers

What is the goal of Step 3 in the Naïve Bayes Classifier?

Signup and view all the answers

What does P(X|H) represent in Bayes' Theorem?

Signup and view all the answers

What is the formula to calculate the Accuracy of a classifier?

Signup and view all the answers

What is the Naïve Bayes Classifier used for?

Signup and view all the answers

What is model evaluation and selection about?

Signup and view all the answers

What is the term for when one class is rare, such as fraud detection or HIV-positive diagnosis?

Signup and view all the answers

What does the Confusion Matrix provide?

Signup and view all the answers

What is Sensitivity in classifier evaluation?

Signup and view all the answers

What is the formula to calculate the Error Rate of a classifier?

Signup and view all the answers

What is Precision in classifier evaluation?

Signup and view all the answers

## Study Notes

### Classifier Evaluation Metrics

- Recall: percentage of positive tuples that the classifier labels as positive, perfect score is 1.0
- Precision: exactness – what percentage of tuples that the classifier labels as positive are actually positive
- Inverse relationship between precision and recall
- F-measure (F1 or F-score): harmonic mean of precision and recall

### Classifier Evaluation Metrics: Example

- Actual class vs predicted class cancer = yes, cancer = no, total recognition, sensitivity, and specificity
- Precision = 90/230 = 39.13%
- Recall = 90/300 = 30.00%

### Evaluating Classifier Accuracy

- Holdout method: given data is randomly partitioned into two independent sets (training and test sets)
- Random sampling: a variation of holdout
- Cross-validation (k-fold, where k = 10 is most popular):
- Randomly partition the data into k mutually exclusive subsets
- At i-th iteration, use Di as test set and others as training set

- Leave-one-out: k folds where k = number of tuples, for small-sized data
- Stratified cross-validation: folds are stratified so that class distribution in each fold is approximately the same as that in the initial data

### Rule-Based Classification

- Using IF-THEN rules for classification
- Rule accuracy and coverage
- Example: Rule R1, which covers 2 of the 14 tuples, with coverage (R1) = 2/14 = 14.28% and accuracy (R1) = 2/2 = 100%

### Naïve Bayes Classification

- A statistical classifier: performs probabilistic prediction, i.e., predicts class membership probabilities
- Foundation: Based on Bayes' Theorem
- Performance: A simple Bayesian classifier has comparable performance with decision tree and selected neural network classifiers
- Incremental: Each training example can incrementally increase/decrease the probability that a hypothesis is correct — prior knowledge can be combined with observed data
- Standard: Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured

### Bayes' Theorem

- P(H | X) = P(X | H)P(H) / P(X)
- Let X be a data sample (“evidence”): class label is unknown
- Let H be a hypothesis that X belongs to class C
- P(H) (prior probability): the initial probability

### Naïve Bayes Classifier Example

- Step 1: Compute the prior probability for each class
- Step 2: Compute P(X|Ci)
- Step 3: Find the class that maximizes P(X|Ci) P(Ci)

### Model Evaluation and Selection

- Evaluation metrics: How can we measure accuracy? Other metrics to consider?
- What if we have more than one classifier and want to choose the “best” one? This is referred to as model selection
- Use validation test set of class-labeled tuples instead of training set when assessing accuracy
- Methods for estimating a classifier's accuracy:
- Holdout method, random subsampling
- Cross-validation
- Bootstrap

### Metrics for Evaluating Classifier Performance: Confusion Matrix

- Confusion Matrix: Actual class vs Predicted class
- Example of Confusion Matrix: Actual class vs Predicted class, total recognition rate
- Given m classes, an entry, CMi,j in a confusion matrix indicates the number of tuples in class i that were labeled by the classifier as class j

### Classifier Evaluation Metrics: Accuracy, Error Rate, Sensitivity, and Specificity

- Classifier accuracy, or the recognition rate: percentage of test set tuples that are correctly classified
- Error rate: 1 – accuracy, or recognition rate
- Sensitivity: True Positive recognition rate
- Specificity: True Negative recognition rate

## Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

## Description

Test your understanding of classification in data mining and warehousing, including decision tree induction, rule-based classification, and accuracy measures. Review key concepts from Chapter 8 of CIS 517.