CIS 517: Data Mining and Warehousing Chapter 8 - Classification
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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?

    <p>To randomly partition the data into two independent sets</p> Signup and view all the answers

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

    <p>The class distribution in each fold</p> Signup and view all the answers

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

    <p>To evaluate the model's performance on small datasets</p> Signup and view all the answers

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

    <p>To predict the class label of a tuple</p> Signup and view all the answers

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

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

    What is the foundation of Naïve Bayes classification?

    <p>Bayes’ Theorem</p> Signup and view all the answers

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

    <p>Incremental</p> Signup and view all the answers

    What is the purpose of Bayes’ Theorem in classification?

    <p>To perform probabilistic prediction</p> Signup and view all the answers

    What is the advantage of using Naïve Bayes classification?

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

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

    <p>The prior probability of a hypothesis</p> Signup and view all the answers

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

    <p>To evaluate the accuracy of a model</p> Signup and view all the answers

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

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

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

    <p>The probability of evidence given a hypothesis</p> Signup and view all the answers

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

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

    What is the Naïve Bayes Classifier used for?

    <p>To classify data samples into different classes</p> Signup and view all the answers

    What is model evaluation and selection about?

    <p>Evaluating the accuracy of a classifier and selecting the best one</p> Signup and view all the answers

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

    <p>Class Imbalance Problem</p> Signup and view all the answers

    What does the Confusion Matrix provide?

    <p>Details of actual class and predicted class</p> Signup and view all the answers

    What is Sensitivity in classifier evaluation?

    <p>True Positive recognition rate</p> Signup and view all the answers

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

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

    What is Precision in classifier evaluation?

    <p>What percentage of tuples that the classifier labeled as positive are actually positive</p> 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.

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