24 Questions
What is the purpose of an ordered rule set?
To assign a test record to a class label based on the highest ranked rule triggered
What is the name given to an ordered rule set?
Decision list
What is the default class assigned to a test record if none of the rules are triggered?
Majority class
What is the main difference between rule-based ordering and class-based ordering?
Rule-based ordering ranks rules by quality, while class-based ordering ranks rules by class
What is the name of the algorithm based on Rule-Based Classification used for learning a disjunctive set of rules?
Sequential Covering
What is the purpose of the Learn-One-Rule function in the Sequential Covering algorithm?
To grow a rule
What is the main advantage of the Direct Method in building classification rules?
It extracts rules directly from data
What is the purpose of the Indirect Method in building classification rules?
To extract rules from other classification models
What is the main objective of using Bayes theorem in classification?
To maximize the posterior probability of the class labels
What is the formula to calculate the posterior probability in Bayes theorem?
P(Y | X) = P(X | Y) * P(Y) / P(X)
What is the goal of Naïve Bayes classification?
To predict the class label that maximizes the posterior probability
What is the advantage of using ensemble classifiers?
To improve the accuracy of the classification model
What is the purpose of evaluating the performance of a classification model?
To compare the performance of different classification models
What is the formula to calculate the precision of a classification model?
Precision = TP / (TP + FP)
What is the purpose of using the F-measure in classification evaluation?
To balance the precision and recall of the classification model
What is the purpose of using Rule-based classification?
To increase the interpretability of the classification model
What is the purpose of using Bayes theorem in Naïve Bayes classification?
To compute the posterior probability of the class labels
What is the advantage of using Bayes theorem in classification?
To provide a probabilistic framework for classification
What is a characteristic of Naïve Bayes classifiers?
Robust to isolated noise points
What is the purpose of a rule in a Rule-Based Classifier?
To classify records by using a collection of “if…then…” rules
What happens when a rule in a Rule-Based Classifier covers a record?
The rule is fired or triggered
What is a limitation of Naïve Bayes classifiers?
They are sensitive to irrelevant attributes
What can be used as an alternative to Naïve Bayes classifiers when dealing with correlated attributes?
Bayesian Belief Networks (BBN)
What is the structure of a rule in a Rule-Based Classifier?
Condition → y, where y is a class label
Study Notes
Classification Techniques
- Base Classifiers:
- Decision Tree based Methods
- Naïve Bayes
- Rule-based Methods
- Bayesian Belief Networks
- Nearest-neighbor
- Support Vector Machines
- Neural Networks, Deep Neural Nets
- Ensemble Classifiers:
- Boosting
- Bagging
- Random Forests
Bayes Classifier
- A probabilistic framework for solving classification problems
- Conditional Probability: P(Y|X) = P(X,Y) / P(X)
- Bayes theorem: P(Y|X) = P(X|Y) * P(Y) / P(X)
Using Bayes Theorem for Classification
- Consider each attribute and class label as random variables
- Goal is to predict class Y given attributes X1, X2, …, Xd
- Want to find the value of Y that maximizes P(Y| X1, X2, …, Xd)
Naïve Bayes
- Robust to isolated noise points
- Handle missing values by ignoring the instance during probability estimate calculations
- Robust to irrelevant attributes
- Redundant and correlated attributes will violate class conditional assumption
Rule-Based Classifier
- Classify records by using a collection of “if…then…” rules
- Rule format: r: (Condition) → y, where Condition is a conjunction of attributes and y is the class label
- A rule covers a record if the precondition of the rule matches the attributes of the record
Rule-Based Classifier (cont.)
- Rule-based Classifier can be Unordered or Ordered
- In Ordered Rule Set, rules are rank ordered according to their priority (accuracy, coverage, total description length, or the order in which the rules are generated)
Building Classification Rules
- Direct Method: Extract rules directly from data (e.g. RIPPER, CN2, Holte’s 1R)
- Indirect Method: Extract rules from other classification models (e.g. decision trees, neural networks)
Direct Method: Sequential Covering
- A popular algorithm based on Rule-Based Classification used for learning a disjunctive set of rules
- Main steps:
- Start from an empty rule
- Grow a rule using the Learn-One-Rule function
- Remove training records covered by the rule
This quiz covers basic concepts and techniques of classification, including Naïve Bayes, Rule-Based, and evaluation methods. It also touches on base classifiers like Decision Trees, Bayesian Belief Networks, and Support Vector Machines.
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