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2. Imagine you are given the task to predict the educational qualification of each person using their demographic data with the following attributes: (1) Annual Income (real-valued), (2) Income Tax filed (real-valued), (3) Age (integer), (4) State of residence in US (categorical), (5) Gender (categorical), (6) House Owner or not (Boolean), and (7) Height (in inches). Assume the target classes are (a) college degree and (b) without a college degree. Also, assume that the fraction of the population that has a

2. Imagine you are given the task to predict the educational qualification of each person using their demographic data with the following attributes: (1) Annual Income (real-valued), (2) Income Tax filed (real-valued), (3) Age (integer), (4) State of residence in US (categorical), (5) Gender (categorical), (6) House Owner or not (Boolean), and (7) Height (in inches). Assume the target classes are (a) college degree and (b) without a college degree. Also, assume that the fraction of the population that has a

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Need help with this data mining question regarding decision trees

**Task 2: Predicting Educational Qualification using Demographic Data** You are tasked with predicting the educational qualification of each person based on demographic data with the following attributes: 1. **Annual Income** (real-valued) 2. **Income Tax filed** (real-valued) 3. **Age** (integer) 4. **State of residence in US** (categorical) 5. **Gender** (categorical) 6. **House Owner or not** (Boolean) 7. **Height** (in inches) **Assumptions:** - The target classes are (a) college degree and (b) without a college degree. - The fraction of the population with a college degree is roughly equal to the fraction that does not have a college degree. **Decision Trees:** - **Strengths:** 1. Easy to interpret and visualize. 2. Handles both numerical and categorical data effectively. - **Weakness:** 1. Prone to overfitting, especially with complex datasets.
Transcribed Image Text:**Task 2: Predicting Educational Qualification using Demographic Data** You are tasked with predicting the educational qualification of each person based on demographic data with the following attributes: 1. **Annual Income** (real-valued) 2. **Income Tax filed** (real-valued) 3. **Age** (integer) 4. **State of residence in US** (categorical) 5. **Gender** (categorical) 6. **House Owner or not** (Boolean) 7. **Height** (in inches) **Assumptions:** - The target classes are (a) college degree and (b) without a college degree. - The fraction of the population with a college degree is roughly equal to the fraction that does not have a college degree. **Decision Trees:** - **Strengths:** 1. Easy to interpret and visualize. 2. Handles both numerical and categorical data effectively. - **Weakness:** 1. Prone to overfitting, especially with complex datasets.
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