What is one-hot encoding?

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One-hot encoding is a technique used in machine learning and data preprocessing to convert categorical data (non-numeric labels) into a numeric format that algorithms can understand.

How it works:

• Each unique category is represented as a binary vector.

• One position in the vector is set to 1 (hot) for the present category, and all others are set to 0.

Example:

Categories: ["Red", "Green", "Blue"]
One-hot encoded form:

cssit
Red   → [1, 0, 0]  
Green → [0, 1, 0]  
Blue  → [0, 0, 1]  

In Python (Pandas):

python

import pandas as pd
df = pd.DataFrame({'Color': ['Red', 'Green', 'Blue']})
encoded = pd.get_dummies(df['Color'])
print(encoded)

Advantages:

• Prevents algorithms from assuming an order between categories.

• Works well with models that need numeric inputs.

Disadvantages:

• Increases dimensionality if many categories exist (curse of dimensionality).

In short: One-hot encoding transforms categorical values into binary vectors, ensuring machine learning models treat categories as separate, unordered entities

Read More:

What is feature engineering?

What is data normalization and standardization?

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