CIS 517: Data Mining and Warehousing Chapter 8

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Questions and Answers

What is the perfect score for recall?

1.0 (correct)

0.5

0.8

0.9

What is the relationship between precision and recall?

Exponential relationship

Inverse proportion (correct)

Direct proportion

No relationship

What is the calculation for precision in the given example?

90/230 (correct)

9560/9700

90/300

210/300

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

To randomly partition the data into two independent sets Signup and view all the answers

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

The class distribution in each fold Signup and view all the answers

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

To evaluate the model's performance on small datasets Signup and view all the answers

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

To predict the class label of a tuple Signup and view all the answers

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

Number of tuples covered / Total number of tuples Signup and view all the answers

What is the foundation of Naïve Bayes classification?

Bayes’ Theorem Signup and view all the answers

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

Incremental Signup and view all the answers

What is the purpose of Bayes’ Theorem in classification?

To perform probabilistic prediction Signup and view all the answers

What is the advantage of using Naïve Bayes classification?

It has comparable performance with decision tree and neural network classifiers Signup and view all the answers

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

The prior probability of a hypothesis Signup and view all the answers

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

To evaluate the accuracy of a model Signup and view all the answers

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

To find the class that maximizes P(X|Ci) P(Ci) Signup and view all the answers

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

The probability of evidence given a hypothesis Signup and view all the answers

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

(TP + TN)/All Signup and view all the answers

What is the Naïve Bayes Classifier used for?

To classify data samples into different classes Signup and view all the answers

What is model evaluation and selection about?

Evaluating the accuracy of a classifier and selecting the best one Signup and view all the answers

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

Class Imbalance Problem Signup and view all the answers

What does the Confusion Matrix provide?

Details of actual class and predicted class Signup and view all the answers

What is Sensitivity in classifier evaluation?

True Positive recognition rate Signup and view all the answers

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

Error Rate = (FP + FN)/All Signup and view all the answers

What is Precision in classifier evaluation?

What percentage of tuples that the classifier labeled as positive are actually positive 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

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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.

CIS 517: Data Mining and Warehousing Chapter 8 - Classification