BaseN Encoding Grid Search in Category Encoders

One of the more interesting encoders in the category_encoders library is the BaseN encoder. The idea behind it is to take a categorical variable and convert it into a series of binary variables, similar to one-hot encoding, but with a different base. For example, if we have a categorical variable with 8 unique values, we could encode it in base 2 with 3 binary variables (2³ = 8).

The advantage of this is that we can potentially represent our categorical variables with fewer features than one-hot encoding would require, while still maintaining the ability to represent all of our categories uniquely. The disadvantage is that we introduce a potentially meaningless ordering to our categories.

Let’s look at a quick example of how we might use grid search to find the optimal base for our encoding. We’ll use the category_encoders library along with scikit-learn’s grid search functionality.

import pandas as pd
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from category_encoders import BaseNEncoder

# Create a sample dataset
X = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'] * 100
})
y = np.random.randint(0, 2, len(X))  # Binary target

# Create pipeline with encoder and classifier
pipe = Pipeline([
    ('encoder', BaseNEncoder()),
    ('clf', RandomForestClassifier())
])

# Define parameter grid
param_grid = {
    'encoder__base': [2, 3, 4, 5]  # Try different bases
}

# Perform grid search
grid = GridSearchCV(pipe, param_grid, cv=5)
grid.fit(X, y)

print(f"Best base: {grid.best_params_['encoder__base']}")
print(f"Best score: {grid.best_score_:.3f}")

This example shows how we can use grid search to find the optimal base for our encoding. The best base will depend on your specific dataset and problem. In general, you’ll want to try a range of bases and see what works best for your case.

Some things to consider when choosing a base:

• The number of unique categories in your variable
• The amount of memory you have available
• The interpretability requirements of your model
• The potential impact of introducing ordering to your categories

The BaseN encoder is just one of many encoding options available in category_encoders. Each has its own strengths and trade-offs, and the best choice will depend on your specific use case.