COMP9517 Pattern Recognition Part 1

Questions and Answers

What does the term 'entropy' refer to in the context of decision trees?

The evaluation of the tree's accuracy

The total number of nodes in a decision tree

The amount of information each event contains

The measure of uncertainty or disorder in a set of events (correct)

Information Gain is a criterion used to define the optimality of decision trees.

True

What is the primary purpose of using measures from information theory in decision tree construction?

To evaluate the optimality of the tree's structure.

In a decision tree, the criterion for optimality is often based on __________.

entropy Signup and view all the answers

Match the following tree structures with their descriptions:

Binary Tree = A tree where each node has at most two children Decision Tree = A flowchart-like structure used for decision making General Tree = A tree structure with no restrictions on the number of children per node Trie = A specialized tree used for storing associative data structures Signup and view all the answers

Which feature selection criterion is often preferred in decision tree algorithms?

Entropy Signup and view all the answers

The construction of decision trees is solely based on training data without any defined criteria.

False Signup and view all the answers

Name one advantage of using decision trees for classification.

They are easy to interpret and visualize. Signup and view all the answers

Which of the following is an advantage of decision trees?

Can handle both numerical and categorical data Signup and view all the answers

Random forests improve the predictive performance of individual decision trees by creating multiple models and averaging their outputs.

True Signup and view all the answers

What is the primary criterion used in decision tree splitting?

Information Gain Signup and view all the answers

The algorithm used in random forests to sample data with replacement is called __________.

bagging Signup and view all the answers

Match the following terms related to decision trees:

Overfitting = When a model learns noise from training data and performs poorly on unseen data Feature Bagging = Selecting a random subset of features at each split Greedy Algorithm = Makes a series of decisions to find a locally optimal solution Entropy = Measures the impurity of a set of data Signup and view all the answers

Which statement about decision trees is true?

They provide information on the importance of features. Signup and view all the answers

Decision trees only create vertical splits based on feature values.

True Signup and view all the answers

Which of the following is a characteristic of decision trees?

Leaf nodes contain the class labels. Signup and view all the answers

Decision trees classify a sample by following a sequence of questions.

True Signup and view all the answers

What is the main purpose of feature selection in decision tree construction?

To identify features that create the best classification tree. Signup and view all the answers

In a binary decision tree, each node selects a left or right branch based on whether the feature value is below or above a __________.

threshold Signup and view all the answers

Match the following terms related to decision trees with their definitions:

Node = Represents a decision point or a feature in the tree Leaf Node = Contains the class label or classification result Threshold = A value used to split the data Branch = Connects nodes and defines the relationship between decisions Signup and view all the answers

What is a disadvantage of using decision trees?

They are computationally expensive. Signup and view all the answers

The choice of priors in Bayesian decision theory is objective and based on empirical evidence.

False Signup and view all the answers

What happens to the correlation and strength of trees when the parameter 𝑚 is reduced?

Correlation decreases, strength decreases Signup and view all the answers

Increasing the strength of individual trees generally decreases the overall forest error rate.

True Signup and view all the answers

List one advantage of using random forests over traditional decision trees.

High accuracy or efficiently handles large datasets. Signup and view all the answers

In random forests, the majority vote of all trees determines the random forest __________.

prediction Signup and view all the answers

Match the type of error with its explanation regarding random forests.

Correlation error = Increased correlation between trees increases the forest error rate Strength error = Weak trees contribute to a higher forest error rate Overfitting error = Complex models fit training data too closely, harming performance on new data Interpretation error = Random forests are less interpretable than individual decision trees Signup and view all the answers

What is a common application of random forests mentioned?

Predicting Alzheimer’s disease Signup and view all the answers

Random forests require feature selection when dealing with thousands of input features.

False Signup and view all the answers

What is the effect of reducing the correlation between trees in a random forest?

It leads to better generalization. Signup and view all the answers

Random forests work effectively with missing values due to their __________ nature.

robust Signup and view all the answers

Study Notes

Bayesian Decision Theory

Probabilities ( p(x|c_i) ) and ( p(c_i) ) can be estimated from samples.
For parametric models, parameters can be learned from samples.
A normal distribution may describe a class, with known covariance matrix ( \Sigma ) and unknown mean ( \mu ).
The mean can be estimated as the average of labeled training samples: ( \hat{\mu} = \bar{x} ).

Bayesian Decision Rule Classifier

Pros:
- Simple and intuitive approach.
- Accounts for uncertainties in data.
- Allows integration of new information with existing knowledge.
Cons:
- Computationally intensive.
- Selection of prior probabilities can be subjective.

Decision Trees: Introduction

Effective for classification problems with real-valued features and some metric.
Handles nominal (categorical) data without natural ordering, e.g., {high, medium, low}.
Rules-based methods can classify both nominal and continuous data.

Decision Trees: Example

Example of fish classification using length ( x_1 ) and width ( x_2 ).
Decision tree splits based on feature values, leading to classifications of salmon or sea bass.

Decision Trees: Summary

Classification involves a sequence of questions based on feature values.
Directed decision tree structure with nodes representing features and leaf nodes containing class labels.
Each branching node has child nodes for possible values of the parent feature.

Decision Trees Construction

Binary decision tree structure using a decision function at each node.
Each node uses a single feature and threshold for splitting.
Multiple valid decision trees may exist for given training samples based on feature selection.
Selecting features is aimed at achieving the "best" tree, often preferring smaller trees.

Decision Trees Construction: Algorithm

Utilizes measures from information theory, such as entropy and information gain.

Constructing Optimal Decision Tree

Optimal decision trees are defined based on minimizing entropy measured by: [ H(y) = -\sum_{i=1}^{P} p(y_i) \log p(y_i) ]
Decision trees are built iteratively by identifying features offering the highest information gain.

Decision Trees: Classifier Pros and Cons

Pros:
- Interpretable and easy to understand.
- Handles both numerical and categorical data.
- Robust against outliers and missing values.
- Provides feature importance for selection.
Cons:
- Prone to overfitting.
- Only permits axis-aligned splits.
- May not find the globally optimal tree due to greedy nature.

Ensemble Learning

Combines multiple models to enhance predictive performance.
Models can vary by using different classifiers, parameters, training examples, or feature sets.

Random Forests

A specific ensemble learning technique that builds multiple decision trees.
Classifications are determined by majority voting from all trees.
Reduces overfitting associated with single decision trees.

Random Forests: Breiman’s Algorithm

Involves random sampling instances and features to build each decision tree.
Trees are constructed to maximum size without pruning.
Predictions from trees are aggregated through majority voting.

Factors Influencing Random Forest Performance

Correlation between trees: High correlation may increase error rates.
Strength of individual trees: Strong trees have lower error rates.
Optimal feature selection parameter ( m ) balances correlation and strength.

Random Forests: Pros and Cons

Pros:
- High accuracy compared to traditional methods.
- Efficient with large datasets and many input features.
- Effectively manages missing values.
Cons:
- Less interpretable than single decision trees.
- More complex and time-intensive to develop.

Random Forests: Application

Used in predicting Alzheimer’s disease based on features such as cortical thickness.

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Description

This quiz covers key concepts in Pattern Recognition, focusing on Bayesian Decision Theory and loss function definitions. Learn how to estimate probabilities and model parameters from empirical data. Get ready to apply these principles in practical scenarios!

COMP9517 Pattern Recognition Part 1

Questions and Answers

What does the term 'entropy' refer to in the context of decision trees?

Information Gain is a criterion used to define the optimality of decision trees.

What is the primary purpose of using measures from information theory in decision tree construction?

In a decision tree, the criterion for optimality is often based on __________.

Match the following tree structures with their descriptions:

Which feature selection criterion is often preferred in decision tree algorithms?

The construction of decision trees is solely based on training data without any defined criteria.

Name one advantage of using decision trees for classification.

Which of the following is an advantage of decision trees?

Random forests improve the predictive performance of individual decision trees by creating multiple models and averaging their outputs.

What is the primary criterion used in decision tree splitting?

The algorithm used in random forests to sample data with replacement is called __________.

Match the following terms related to decision trees:

Which statement about decision trees is true?

Decision trees only create vertical splits based on feature values.

Which of the following is a characteristic of decision trees?

Decision trees classify a sample by following a sequence of questions.

What is the main purpose of feature selection in decision tree construction?

In a binary decision tree, each node selects a left or right branch based on whether the feature value is below or above a __________.

Match the following terms related to decision trees with their definitions:

What is a disadvantage of using decision trees?

The choice of priors in Bayesian decision theory is objective and based on empirical evidence.

What happens to the correlation and strength of trees when the parameter 𝑚 is reduced?

Increasing the strength of individual trees generally decreases the overall forest error rate.

List one advantage of using random forests over traditional decision trees.

In random forests, the majority vote of all trees determines the random forest __________.

Match the type of error with its explanation regarding random forests.

What is a common application of random forests mentioned?

Random forests require feature selection when dealing with thousands of input features.

What is the effect of reducing the correlation between trees in a random forest?

Random forests work effectively with missing values due to their __________ nature.

Study Notes

Bayesian Decision Theory

Bayesian Decision Rule Classifier

Decision Trees: Introduction

Decision Trees: Example

Decision Trees: Summary

Decision Trees Construction

Decision Trees Construction: Algorithm

Constructing Optimal Decision Tree

Decision Trees: Classifier Pros and Cons

Ensemble Learning

Random Forests

Random Forests: Breiman’s Algorithm

Factors Influencing Random Forest Performance

Random Forests: Pros and Cons

Random Forests: Application

Studying That Suits You

Description

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