Why do we convert values of Categorical Variable to numerical values? _nswer:
Why do we convert values of Categorical Variable to numerical values? _nswer:
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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![### Converting Categorical Variables into Numerical Values
**Question:**
Why do we convert values of Categorical Variables into numerical values?
**Answer:**
[Text Box for Entry]
---
**Explanation:**
This question is frequently addressed in the context of data science and machine learning. Categorical variables often need to be converted into numerical values to be utilized in algorithms that require numerical input for analysis, computation, and prediction. This conversion process, known as encoding, allows for better data processing and model performance.
Common techniques for converting categorical variables include:
1. **Label Encoding:** Assigns a unique numerical value to each category.
2. **One-Hot Encoding:** Creates binary columns for each category, indicating the presence of each category with 0 or 1.
3. **Binary Encoding:** Combines aspects of both label encoding and one-hot encoding for high-cardinality data transformation.
**Educational Context:**
Understanding why and how to convert categorical data can have a significant impact on the accuracy and effectiveness of predictive models. This fundamental knowledge is crucial for anyone working in data-driven fields.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fffeb04d3-7a80-49ff-94bd-a2567d496b56%2F2d0b5cf9-094f-4b4b-8fc9-5aaecffe33c9%2Fed2m0ze_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Converting Categorical Variables into Numerical Values
**Question:**
Why do we convert values of Categorical Variables into numerical values?
**Answer:**
[Text Box for Entry]
---
**Explanation:**
This question is frequently addressed in the context of data science and machine learning. Categorical variables often need to be converted into numerical values to be utilized in algorithms that require numerical input for analysis, computation, and prediction. This conversion process, known as encoding, allows for better data processing and model performance.
Common techniques for converting categorical variables include:
1. **Label Encoding:** Assigns a unique numerical value to each category.
2. **One-Hot Encoding:** Creates binary columns for each category, indicating the presence of each category with 0 or 1.
3. **Binary Encoding:** Combines aspects of both label encoding and one-hot encoding for high-cardinality data transformation.
**Educational Context:**
Understanding why and how to convert categorical data can have a significant impact on the accuracy and effectiveness of predictive models. This fundamental knowledge is crucial for anyone working in data-driven fields.
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