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Questions and Answers
What is a primary characteristic of a good decision tree?
What is a primary characteristic of a good decision tree?
In the case study predicting heart attacks, what was the main objective?
In the case study predicting heart attacks, what was the main objective?
Which algorithm was utilized in the heart attack prediction case study?
Which algorithm was utilized in the heart attack prediction case study?
What does the acronym CART stand for in the context of decision trees?
What does the acronym CART stand for in the context of decision trees?
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Before applying the CART algorithm in the case study, which processes were essential?
Before applying the CART algorithm in the case study, which processes were essential?
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What percentage of cases did the decision tree predict correctly in the heart attack study?
What percentage of cases did the decision tree predict correctly in the heart attack study?
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Which of the following is NOT a key element in constructing a decision tree?
Which of the following is NOT a key element in constructing a decision tree?
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Which type of decisions are decision trees most convenient for?
Which type of decisions are decision trees most convenient for?
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What is a fundamental advantage of decision trees in data analysis?
What is a fundamental advantage of decision trees in data analysis?
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What do decision trees primarily rely on to reach a conclusion?
What do decision trees primarily rely on to reach a conclusion?
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Study Notes
Decision Trees Overview
- Decision trees are a supervised classification technique
- They're a simple way to guide a decision-making process
- Decisions can be complex or straightforward
- These techniques use hierarchically branched structures
- Decisions are made by asking questions in a hierarchical order
- A good decision tree is concise providing a decision via relevant questions
- They can be created from small datasets and applied to broader populations
- Decision trees are convenient for simple binary decisions
Learning Objectives
- Understanding decision trees
- Identifying key elements for constructing a decision tree
- Creating a decision tree using a simple dataset
- Identifying popular decision tree algorithms
Case Study: Predicting Heart Attacks
- The case study used data mining to predict heart attacks
- Data was based on patients who had previously suffered a heart attack
- The goal was to predict which patients were at risk of a second attack within 30 days
- This information aided in determining treatment plans
- The CART algorithm was applied to the data
- Data included more than 100 variables and required transformation and cleansing
- Variables like blood pressure, age, and sinus problems are considered
- The decision tree predicted 86.5% of cases accurately
Results
- Low blood pressure (≤90) indicated a high risk of another heart attack (70%)
- If blood pressure was okay, age was the next factor to consider
- Age under 62 led to almost guaranteed survival (98%)
- Those older required sinus problem evaluation
- If sinus was okay, survival was 89%, otherwise 50%
- The decision tree predicted 86.5% of cases correctly
Disease Diagnosis
- A doctor's questioning of a patient and decisions regarding treatment, plus recommended tests, can be seen as a decision tree in practice
- Decision trees and rules are used for diagnosing diseases
- Each question branches into possible outcomes
- The process continues until a leaf node is reached
- Medical experts and other experts employ similar processes to solve problems
Machine Learning and Decision Trees
- Machine learning involves using past data to create knowledge
- Decision trees utilize machine learning algorithms for abstracting knowledge from data
- The accuracy of a decision tree can be measured based on the frequency of correct predictions
Decision Trees: Points to Consider
- Accuracy increases with more training data
- A better decision tree is more frugal, using fewer variables for the correct decision
- Fewer questions to get the right decision
- Minimal effort required
Exercise: Predicting Play
- The exercise focused on predicting if a game should occur based on atmospheric conditions
- The dataset includes factors like Outlook, Temperature, Humidity, Windy condition, and Play to be determined.
- The suggested methodology is to examine past data for similar circumstances to help reach a decision
Data Set Analysis
- Examining dataset records for decisions based on similar conditions is helpful in making a decision
- Realistically, data may not always be accessible or consistent
- Using a decision tree would be helpful to abstract the data information
Decision Tree Construction
- Determine the root node in a decision tree
- Splitting the tree
- Determining the next nodes in the tree
Determining the Root Node
- Identifying the most crucial question to address the problem.
- Determining the importance of questions.
- Identifying the root node for a decision tree
- Examining the available choices
Error and Rules
- Error measures the accuracy of the decision tree based on incorrect predictions
- Rules represent paths in a decision tree, detailing the conditions for predictions
- Balancing complexity to avoid overfitting or underfitting is essential
Splitting Criteria
- Choosing a metric to evaluate the importance of each feature in a decision tree
- Example metrics for splitting are information gain or Gini impurity, and Chi-square
Determining the Root Node: Examples
- Analyzing the Outlook variable errors for correctness
- Analyzing the Temperature variable errors for correctness
- Analyzing the Humidity variable errors for correctness
- Analyzing the Windy variable errors for correctness
Determine Root Node of a Decision Tree
- The variable that results in the fewest errors is selected as the root node
- The variable or variables with the most correct decisions are chosen in cases of a tie
- The resulting subtrees' purity is evaluated
Splitting the Tree
- Dividing the data into parts based on the root node's variable evaluation outcomes
Determine Next Nodes
- Identifying the decision to be made next when deciding the next node(s) in the tree
Decision Tree Example
- Illustrating the application with a weather example predicting the possibility of playing or not playing given various weather conditions
Lessons from Constructing Trees
- Decision trees have varying accuracy levels
- Decision trees can successfully operate with generally applicable settings
- Decision trees make use of a limited number of variables for the needed decisions
Observations about the Data
- Decision trees with zero errors are not typical of real-world scenarios
- Real-world decision scenarios may not be immediately obvious
- Decision trees strive to employ the least amount of required variables
Observations about the Data (Continued)
- Increasing sub-trees can increase predictive accuracy, but this can reduce readability
- Perfectly fitted trees may overfit the data, not predicting the future well
Decision Tree Algorithms
- Using a divide and conquer technique to construct decision trees
- Steps for creating a decision tree, in pseudo code format
- Steps for constructing decision trees
Decision Tree Algorithms: Key Elements
- Factors for deciding what variables to use for the first split
- Methods or values when dealing with continuous variables
- Variables used for constructing tree components
- Decision points or stopping criteria
Key Elements: Pruning
- Trimming the decision tree for improved balance
- Pruning occurs after the tree has been created to improve usability
- Symptoms of overfitting and how to avoid them
Most Popular Decision Tree Algorithms
- Common decision tree methods
- C4.5, CART, and CHAID are examples of popular decision tree methods
What Have We Learned?
- Decision trees are popular data mining tools
- Decision trees offer high predictive accuracy
- Decision trees assist with creating less error-prone models with smaller data sets
- Decision trees are good for effective communication
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Description
This quiz covers the fundamentals of decision trees, highlighting their role in supervised classification and decision-making processes. It explores key elements for constructing decision trees, popular algorithms, and an application case study on predicting heart attacks using data mining techniques.
