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What is the purpose of one-hot encoding in feature engineering?
Practice Questions
Q1
What is the purpose of one-hot encoding in feature engineering?
To normalize numerical features
To convert categorical variables into a numerical format
To reduce dimensionality
To handle missing values
Questions & Step-by-Step Solutions
What is the purpose of one-hot encoding in feature engineering?
Steps
Concepts
Step 1: Understand that categorical variables are non-numeric data, like colors or names.
Step 2: Realize that machine learning algorithms work better with numbers.
Step 3: Learn that one-hot encoding transforms each category into a separate binary column.
Step 4: For each category, create a column that has a '1' if the category is present and '0' if it is not.
Step 5: Use the new binary columns in your machine learning model instead of the original categorical variable.
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