Untitled Quiz

Decision Trees Overview

• Decision trees are a widely used supervised classification technique.
• They guide decision-making through a hierarchical structure of questions.
• Decisions can be simple or complex.
• A good decision tree is concise, reaching a decision with the most relevant questions.
• They are suitable for smaller datasets and can be applied to broader populations.
• Decision trees are well-suited for simple binary decisions.

Learning Objectives

• Understanding decision trees.
• Identifying key elements for constructing a decision tree.
• Creating a decision tree from a simple dataset.
• Identifying popular decision tree algorithms.

Case Study: Predicting Heart Attacks

• The case study uses data mining to predict 30-day heart attack risk.
• Input data includes over 100 variables (e.g., blood pressure, age).
• The CART algorithm was used for prediction.
• Data transformation and cleansing were performed before running the algorithm.
• Features like blood pressure, age, and sinus problems influence the decision tree's questions.
• The decision tree achieves 86.5% accuracy in predicting heart attack cases.
• Examples of factors used in decision questions; Blood pressure low (<=90), patient's age, sinus problems.

Results

• Low blood pressure (≤90) indicates a high risk of another heart attack (70%).
• If blood pressure is normal, age becomes the next factor considered.
• An age below 62 correlates to a high probability of survival (98%).
• If age is above 62, sinus problems become the next relevant filter.
• A healthy sinus indicates an 89% chance of survival, otherwise, it drops to 50%.
• The decision tree correctly predicts 86.5% of the cases.

Disease Diagnosis

• Applying similar logic (using decision trees) is effective in disease diagnosis.
• Doctor-patient conversations, symptom analysis and tests correlate to building a decision tree.
• Potential answers to each question lead to different branches in the decision tree.
• This process continues until a leaf node is reached.
• Medical experts use this method.

Machine Learning and Decision Trees

• Machine learning involves using past data to extract knowledge and rules.
• Decision trees utilize machine learning algorithms to abstract knowledge from data.
• Predictive accuracy is determined by checking how often the correct decisions are made.

Decision Trees: Important Considerations

• Accuracy in decision trees improves with more training data.
• A good tree should use the fewest variables to get the right decision.
• Fewer questions lead to easier decision processes.

Exercise: Predicting Play Given Atmospheric Conditions

• The exercise involves predicting whether to play a game based on weather conditions.
• The exercise requires analyzing historical weather data to guide the decision.

Data Set Analysis

• No exact past cases are present.
• Analysing past data directly isn't feasible.
• Building a decision tree from data is the effective approach.
• Relevant variables in creating a decision tree.
• Not all variables in the data set are required.

Constructing a Decision Tree: Steps

• Determine the root node.
• Splitting the decision tree.
• Define the next nodes in the tree.

Root Node Determination:

• The key is to identify the most relevant question to solve the problem.
• Understanding methods for determining the importance of different questions.
• Identifying the question that leads to the most useful decision tree.
• Selecting questions that provide clear insight or shortest conclusion.

Error Measures and Rules

• Error measures indicate the accuracy of the decision tree, emphasizing the balance needed between complexity and accuracy to avoid overfitting or underfitting.
• Rules detail logical steps in the decision tree, outlining the conditions under which predictions are made.

Splitting Criterion

• Selecting the best variable to initially split data.
• Information gain, entropy and Gini impurity are common evaluation metrics.
• Methods to help analyze data split and impurity levels.
• Chi-Square for assessing statistical significance.

Determining the Root Node: Example Analysis of Variables

• Analysis of initial variable choices.
• Examples include Outlook, Temperature, Humidity, and Windy as important considerations.
• Demonstrates metrics for identifying the most critical node and how these are calculated.

Determining the Root Node: Analyzing Specific Variables

• Specific examples analyzing the variables (outlook, temperature, humidity, and windy) and associated rules.
• How these variables lead to the root node for the decision tree.

Determining the Root Node: Selecting Final Variable

• Selecting the ultimate root node of the decision tree.
• Based on the values from previous analyses of the specific variables.

Splitting the Tree

• Data is divided into segments based on the root node's value.
• Further analysis is performed on each of the segments.

Determining the Next Nodes: Sunny, Rainy Branches

• Identifying the decision tree nodes.
• Steps are the same for each branch.

Decision Tree

• Final decision tree is displayed.
• Using the example weather prediction.
• Example output and the algorithm used to develop the prediction.

Lessons from Constructing Trees

• Comparison of Decision Trees with Table Lookups.
• Various factors of Decision Trees vs Table lookups.
• Accuracy, generality, and simplicity aspects for both methods.

Observations about the Data

• Understanding dataset limitations and caveats.
• Decision tree accuracy limitations with real-life data.
• Identifying issues that may exist in real-life decision trees.

Decision Tree Algorithms

• Describing the divide and conquer method used by decision trees.
• Pseudocode for building decision trees.
• Steps for constructing decision trees.

Decision Tree Algorithms: Key Elements

• Splitting criteria: Selecting the most significant variables for initial splits.
• Measures for determining the proper split (entropy, gain ratio, Gini index).
• Considerations for continuous variables and generating meaningful bins.

Key Elements, Pruning

• Pruning techniques to balance deep and complex decision trees.
• Explanation of pre-pruning and post-pruning methods and criteria.
• Steps for handling overly complex trees.

Most Popular Decision Trees Algorithms

• Listing important algorithms used in building decision trees.
• C 4.5, CART, and CHAID algorithms are prominent examples.

What Have We Learned

• Summary of key points from the study on decision trees.
• Advantages and applications of applying decision trees.