Decision Trees and Classification Rules

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Questions and Answers

What separates the two classes in a binary classification problem using linear models?

Line of best fit

Decision tree

Decision boundary (correct)

Regression line

In decision trees, what is usually tested at the nodes?

The predicted probabilities

A random selection of attributes

The overall classification accuracy

An attribute compared to a constant (correct)

What typically happens to the outcome for a numeric attribute in a decision tree?

It is not re-tested once used.

It results in two branches based on a comparison. (correct)

It results in three branches for each possible value.

It is treated as a constant.

What should be done with missing values when using decision trees?

They may be assigned their own specific branch. Signup and view all the answers

How does a regression tree differ from a linear equation in terms of size and complexity?

Regression trees are much larger and more complex. Signup and view all the answers

What does a model tree combine to generate its predictions?

Linear equations with regression trees Signup and view all the answers

What is a characteristic of leaf nodes in decision trees used for regression?

They contain the average of training instances reaching that branch. Signup and view all the answers

What is a significant advantage of regression trees compared to linear models?

They can model non-linear relationships more accurately. Signup and view all the answers

What characterizes the antecedent in classification rules?

It usually involves a conjunction or a general logic expression. Signup and view all the answers

What problem can arise with unordered rule sets?

They may conflict without a resolution strategy. Signup and view all the answers

What is the primary limitation of classification rules compared to association rules?

Classification rules can only predict specific outcomes, not any attribute. Signup and view all the answers

What is typically sacrificed in ordered rule sets?

Modularity of each rule as an independent nugget. Signup and view all the answers

Why are rules with exceptions beneficial in a ruleset?

They allow for incremental modifications to accommodate new information. Signup and view all the answers

What does a classification rule do when no classifications apply to a given example?

It can classify based on the most popular class from the training set. Signup and view all the answers

What does the term 'replicated subtree problem' refer to in the context of rules?

The complexity of rulesets requiring pruning. Signup and view all the answers

Which of the following statements about support and confidence in association rules is correct?

Support measures frequency, confidence measures reliability of rules. Signup and view all the answers

What is a significant feature of exceptions in rule-based classification?

They can lead to a structure resembling a tree. Signup and view all the answers

Which aspect of nearest-neighbor learning is important for its implementation?

Normalizing all attributes for consistency. Signup and view all the answers

What does instance-based representation fail to explicitly represent?

The structures learned from the data. Signup and view all the answers

How do rectangular regions in instance-based methods relate to rules?

They are similar to rules with conditions based on numeric attributes. Signup and view all the answers

What is a drawback of instance-based representation in machine learning?

It does not explicitly define the regions of classes. Signup and view all the answers

In instance selection, why might it be unnecessary to store all training instances?

Some regions are more stable and class boundaries are clearer. Signup and view all the answers

What is one common misconception about rules in machine learning?

Rules always cover all possible instances. Signup and view all the answers

What is the main advantage of using exceptions in complex rule sets?

They allow for more nuanced and expressive rules. Signup and view all the answers

Study Notes

Output Knowledge Representation

Tables are used to represent knowledge, such as weather data
Decision tables or regression tables are examples
Tables may involve selecting a subset of attributes

Output: Linear Model

Linear models, like CPU performance, can be used
In binary classification problems, the model produces a line that separates the two classes (decision boundary)
The line can be compared to a hyperplane in higher dimensions

Trees

Decision trees are used for knowledge representation (contact lens, labor)
Nodes test attributes, often by comparisons with constant values
Leaf nodes provide classifications or probabilities
Nominal attributes are not usually re-tested while numerical attributes may be re-tested later
Testing numerical attributes creates two branches (< constant, >= constant), but there is also a third for missing values
For real-valued data, exact equality is rare
Intervals are often specified (Above, Within, Below)
Missing values have their own branches
Decision trees allow for alternative splits (option nodes)

Decision Trees for Regression

For numeric outcomes, each leaf node contains the average of training instances reaching that branch (regression tree)
Regression trees are more complex
However, generally more accurate than linear equations, especially for non-linear data
Tree structures are more complex than linear equations, but are usually more accurate for data that aren't perfectly linear
More complex than linear equations but easier for comprehension

Model Tree

Combining regression equations and regression trees is possible
Leaves in a model tree contain linear expressions
Model trees model continuous functions using linear patches
Model trees are smaller and easier to understand, often more accurate

Output: Rules

Antecedents form the conditions (often conjunctions)
Consequents are classes or probability distributions
This system includes Classification Rules and Association Rules

Classification Rules

Classification rules can be derived directly from a decision tree
Rule sets can be more complex than necessary
Simplifying the ruleset is possible through pruning
It isn't always easy to generate a tree from rules

Replicated Subtree Problem

A decision tree may contain replicated subtrees, leading to inefficiency

Exclusive-Or Problem

This problem is exemplified by a figure, illustrating patterns of dependent conditional statements

Rules

Rules are a popular choice due to the independent nature of each new rule
Individual rules can be added to an existing ruleset without needing to re-structure the entire set
However, rules may have conflicts, requiring order or a conflict resolution algorithm for an ordered rule set or an unordered rule set, requiring conflict resolution

Unordered Rulesets

Multiple possible classifications exist for a given example
A lack of classification or using the most probable outcome from training data
No classification applies: using the most frequent class

Simple Example

Boolean classes don't allow conflicts.
All outcomes are expressed with the assumption of a closed world.

Exceptions

Fixing rule sets is more complex than expected
Changing boundaries might re-classify existing examples and some previously good exceptions could be bad exceptions.
Expert input may be required to identify and fix exceptions
Exception clauses can be established to adjust rule sets incrementally

More Expressive Rules

Further examples are presented elsewhere to illustrate more expressive rules

Instance-Based Representation

Lazy learning vs. eager learning (nearest-neighbor k-value)
Distance metric (Euclidean distance often used with normalized data)
Nominal attributes require considerations in the distance metric
Training instance selection

Instance Selection

Not all training instances must be stored
Regions of attribute space are more stable than others
Fewer samples are needed for stable regions
More exemplars near class boundaries

IBL (Instance-based Learning)

Instance-based methods don't explicitly detail the learning structure

Rectangular Regions

Rectangular regions are similar to rules but often are more conservative

Prototypes

Nearest-Prototype method(s)
Using Prototypes instead of Neighbors

Cluster

When using clustering rather than classification, the output is a diagram showing how instances group.

Multiple Membership

Instances can belong to more than one cluster.
Data represented with Venn Diagrams

Probabilities or Fuzzy Memberships

Clusters can be associated with probabilities or fuzzy membership degrees

Hierarchical Clusters

Some algorithms implement hierarchical structures
Showing the hierarchical relationship is often shown with a dendrogram

Clustering

Clustering operations might be followed by stages that associate instances to clusters using rulesets or decision trees

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Description

This quiz explores key concepts related to decision trees and classification rules in machine learning. It covers the specifics of node testing, handling missing values, and the differences between regression trees and linear models. Test your knowledge on how these models are structured and function in various scenarios.