What is the purpose of using one-hot encoding in feature engineering?
Practice Questions
Q1
What is the purpose of using one-hot encoding in feature engineering?
To reduce the number of features
To convert categorical variables into numerical format
To increase the interpretability of the model
To improve model training speed
Questions & Step-by-Step Solutions
What is the purpose of using one-hot encoding in feature engineering?
Step 1: Understand that some data is categorical, meaning it represents categories or groups (like colors: red, blue, green).
Step 2: Realize that most machine learning algorithms work better with numbers rather than words.
Step 3: Learn that one-hot encoding transforms each category into a separate binary column (0 or 1).
Step 4: For example, if you have three colors (red, blue, green), one-hot encoding creates three columns: one for red, one for blue, and one for green.
Step 5: If a data point is red, it will be represented as (1, 0, 0); if it's blue, it will be (0, 1, 0); and if it's green, it will be (0, 0, 1).
Step 6: This numerical representation allows machine learning algorithms to understand and process the categorical data effectively.
