In machine learning, converting categorical data into binary classifications is essential for preparing data for algorithms that require numerical input. Here are various methods you can use:

Basic Encoding

• One-Hot Encoding
• Label Encoding
• Dummy Encoding

Ordered Encoding

• Ordinal Encoding
• Thermometer Encoding

Target-Based Encoding

• Target Encoding (Mean Encoding)
• K-Fold Target Encoding
• Leave-One-Out Encoding
• Mean Encoding with Smoothing
• M-estimate Encoding
• James-Stein Encoding
• CatBoost Encoding

Binary and Base-N Encodings

• Binary Encoding
• BaseN Encoding
• Hash Encoding (Feature Hashing)

Frequency and Count Encoding

• Frequency Encoding
• Count Encoding

Contrast and Statistical Encodings

• WoE (Weight of Evidence) Encoding
• Helmert Encoding
• Backwards Difference Encoding
• Polynomial Encoding
• Deviation (Sum) Encoding

Advanced / Deep Learning Encoding

• Entity Embedding

Encoding LIBRARY

Here's a comprehensive table detailing the encoding methods, their availability in libraries, and relevant information:

Details on Libraries for Unsupported Methods

1. Scikit-learn:
• Provides foundational encoders like LabelEncoder and OneHotEncoder.
• Can be extended with transformers for custom encoding logic.
2. Category Encoders:
• A library focused on categorical encoding methods, including specialized ones like Target Encoding, Hash Encoding, and WoE Encoding.
• Install with pip install category_encoders.
3. Feature Engine:
• Another library that provides prebuilt transformers for some encoding techniques.
• Install with pip install feature-engine.

Encoding Methods for Categorical Features

1. Basic Encoding

2. Ordered Encoding

Install Libraries

bash
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pip install category_encoders
pip install feature-engine

DATA EXAMPLE

# Sample data
df = pd.DataFrame({
    'color': ['red', 'blue', 'green', 'blue', 'green', 'red', 'green'],
    'city': ['London', 'Paris', 'New York', 'Paris', 'London', 'New York', 'London'],
    'target': [1, 0, 1, 0, 1, 0, 1]
})

The initial DataFrame df that was created before any encoding looks like this:

SUMMARY

What is Cardinality in Machine Learning & Data Science?

Definition

Cardinality refers to the number of unique values in a categorical or numerical feature of a dataset.

• Low Cardinality → A feature has a few unique values (e.g., "Gender" with only Male, Female).
• High Cardinality → A feature has many unique values (e.g., "Customer ID" with thousands of unique values).

Examples of Cardinality

Why is Cardinality Important?

1️⃣ It Affects Encoding Methods

• Low Cardinality → Use One-Hot Encoding
• High Cardinality → Use Frequency Encoding, Binary Encoding, or Target Encoding

2️⃣ Impacts Model Performance

• High-cardinality features can increase memory usage and slow down training.
• Too many unique categories may lead to overfitting in models.

3️⃣ Impacts Feature Engineering

• High-cardinality categorical features often contain useful information (e.g., product categories in sales prediction).
• Proper handling ensures that the model generalizes well.

Handling High-Cardinality Features

✅ One-Hot Encoding (Only for low-cardinality features)

✅ Label Encoding (For ordinal categories)

✅ Binary Encoding (Efficient for mid-to-high cardinality)

✅ Frequency Encoding (When category frequency is meaningful)

✅ Target Encoding (For supervised learning tasks)

Conclusion

Cardinality is a crucial factor in data preprocessing. Identifying whether a feature has low, medium, or high cardinality helps in choosing the right encoding method to improve model performance! 🚀