Rule-Based Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

Binning

Discretization

Feature scaling

One-hot encoding

Decision Trees

Association Rule Learning

Sequential Pattern Mining

k-Nearest Neighbors

Decision Trees

Association Rule Learning

Sequential Rule Learning

k-Nearest Neighbors

Lower accuracy

Lower interpretability

Higher training time

Higher prediction time

To create more specific rules from the training data

To create more general rules from the training data

To optimize the weights of a neural network

To directly use the training instances for classification

Rule ranking based on confidence

Rule ranking based on support

Rule ranking based on lift

All of the above

Support

Confidence

Lift

All of the above

To improve the accuracy of the rule set

To improve the interpretability of the rule set

To reduce the complexity of the rule set

To reduce the prediction time

High interpretability

Robustness to noisy data

Scalability to large datasets

Ability to handle missing values

To simplify the rules after they have been induced

To optimize the weights of a neural network

To directly use the training instances for classification

To create a model from the training data

Apriori

Sequential Pattern Mining

RIPPER

ID3

To reduce the complexity of the rule set

To increase the coverage of the rule set

To improve the accuracy of the rule set

To improve the interpretability of the rule set

Two rules that predict the same class label

Two rules that predict different class labels for the same instance

Two rules that have the same antecedent but different consequents

Two rules that have different antecedents but the same consequent

The number of instances correctly classified by a rule

The proportion of instances in the dataset that satisfy the rule's antecedent

The proportion of instances in the dataset that satisfy both the rule's antecedent and consequent

The interestingness of a rule

Lower accuracy

Lower interpretability

Higher training time

Higher prediction time

To extract meaningful rules from the training data

To create a model from the training data

To directly use the training instances for classification

To optimize the weights of a neural network

Support

Confidence

Lift

All of the above

Decision tree learning creates a tree structure, while rule induction creates a set of if-then rules

Decision tree learning creates a set of if-then rules, while rule induction creates a tree structure

Decision tree learning discovers relationships between variables, while rule induction predicts a class label

Decision tree learning predicts a class label, while rule induction discovers relationships between variables

To discover interesting relationships between variables in large datasets

To discover interesting relationships between events in sequences

To create a model for classification or prediction

To optimize the weights of a neural network

To measure the accuracy of a rule

To measure the interestingness of a rule

To measure the support of a rule

To measure the complexity of a rule

Decision tree learning

Association rule learning

Sequential rule learning

k-Nearest Neighbors

Classification rules predict a class label, while association rules discover relationships between variables

Classification rules discover relationships between variables, while association rules predict a class label

Classification rules use a tree structure, while association rules use a graph structure

Classification rules use a graph structure, while association rules use a tree structure

To discover interesting relationships between variables in large datasets

To create a model for classification or prediction

To directly use the training instances for classification

To optimize the weights of a neural network

To increase the complexity of the tree

To improve the interpretability of the tree

To reduce overfitting

To increase the size of the tree

A sequence of if-then rules

A tree structure that represents rules for classification or prediction

A set of association rules

A graph representing the relationships between instances

They can be prone to overfitting

They have lower accuracy compared to other algorithms

They are not suitable for large datasets

They are not interpretable

Decision Trees

Association Rule Learning

Sequential Rule Learning

k-Nearest Neighbors

Higher accuracy

Faster training time

Interpretability of the learned rules

Better handling of noisy data

To create rules from the training data

To optimize the weights of a neural network

To directly use the training instances for classification

To create a model from the training data

Decision Trees

k-Nearest Neighbors

Support Vector Machines

Neural Networks