Target encoding in ML Python

Introduction

Target encoding helps turn categories into numbers by using the average of the target values. This makes it easier for models to understand and use categorical data.

When you have categorical data with many unique values (like zip codes or product IDs).

When you want to keep useful information from categories related to the target.

When one-hot encoding would create too many new columns and slow down the model.

When you want to improve model performance by using target-related information.

When preparing data for models that only accept numbers, like linear regression.

Syntax

ML Python

from category_encoders import TargetEncoder

encoder = TargetEncoder(cols=['category_column'])
encoded_data = encoder.fit_transform(X, y)

You need to install the category_encoders package first using pip install category_encoders.

cols specifies which columns to encode. X is your features, and y is the target variable.

Examples

Encode the 'color' column using target encoding.

ML Python

from category_encoders import TargetEncoder
encoder = TargetEncoder(cols=['color'])
encoded_X = encoder.fit_transform(X, y)

Encode multiple categorical columns 'city' and 'brand' at once.

ML Python

encoder = TargetEncoder(cols=['city', 'brand'])
encoded_X = encoder.fit_transform(X, y)

Use the already fitted encoder to transform new data without changing the encoding.

ML Python

encoded_X = encoder.transform(new_X)

Sample Model

This example shows how to use target encoding on a simple dataset with a 'color' category and a binary target. We encode the 'color' column, train a logistic regression model, and check accuracy.

ML Python

import pandas as pd
from category_encoders import TargetEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Sample data with categorical feature and binary target
data = pd.DataFrame({
    'color': ['red', 'blue', 'green', 'blue', 'red', 'green', 'red', 'blue'],
    'target': [1, 0, 1, 0, 1, 0, 1, 0]
})

X = data[['color']]
y = data['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Create and fit target encoder
encoder = TargetEncoder(cols=['color'])
X_train_encoded = encoder.fit_transform(X_train, y_train)
X_test_encoded = encoder.transform(X_test)

# Train a simple model
model = LogisticRegression()
model.fit(X_train_encoded, y_train)

# Predict and evaluate
preds = model.predict(X_test_encoded)
acc = accuracy_score(y_test, preds)

print(f"Encoded training data:\n{X_train_encoded}\n")
print(f"Predictions: {preds}")
print(f"Accuracy: {acc:.2f}")

Important Notes

Target encoding can cause overfitting if not done carefully. Use techniques like cross-validation or smoothing.

Always fit the encoder only on training data, then transform test data to avoid data leakage.

Target encoding works best with categorical features that have a meaningful relationship with the target.

Summary

Target encoding converts categories into numbers using the average target value.

It helps models use categorical data without creating many new columns.

Be careful to avoid overfitting by fitting only on training data.

Practice

1. What is the main purpose of target encoding in machine learning?

easy

A. Remove missing values from the dataset

B. Normalize numerical features to a 0-1 scale

C. Create new categorical features by combining existing ones

D. Convert categorical variables into numbers using the average target value

Solution

1. Step 1: Understand what target encoding does

   Target encoding replaces categories with the average value of the target variable for each category.

2. Step 2: Compare with other options

   Normalization scales numbers, missing value removal cleans data, and feature creation combines categories, none of which describe target encoding.

3. Final Answer:

   Convert categorical variables into numbers using the average target value -> Option D

4. Quick Check:

   Target encoding = average target per category [OK]

Hint: Target encoding uses target averages to convert categories [OK]

Common Mistakes:

• Confusing target encoding with normalization
• Thinking target encoding creates new categories
• Assuming target encoding removes missing data

2. Which of the following Python code snippets correctly applies target encoding using pandas for a training dataset train_df with categorical column cat_col and target target?

easy

A. mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target)

B. train_df['cat_encoded'] = train_df['cat_col'].astype('category').cat.codes

C. train_df['cat_encoded'] = train_df['target'].mean()

D. train_df['cat_encoded'] = train_df['cat_col'].apply(lambda x: len(x))

Solution

1. Step 1: Identify correct target encoding method

   Target encoding maps each category to the mean target value for that category, done by grouping and mapping.

2. Step 2: Check code correctness

   mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target) groups by category, calculates mean target, then maps it back correctly. Other options do not compute mean target per category.

3. Final Answer:

   mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target) -> Option A

4. Quick Check:

   Group by category and map mean target [OK]

Hint: Group by category and map mean target for encoding [OK]

Common Mistakes:

• Using category codes instead of target mean
• Assigning overall mean target to all rows
• Mapping category length instead of target mean

3. Given the following code, what will be the output of print(test_df['cat_encoded'].tolist())?

import pandas as pd
train_df = pd.DataFrame({'cat_col': ['A', 'B', 'A', 'C'], 'target': [1, 0, 1, 0]})
mean_target = train_df.groupby('cat_col')['target'].mean()
test_df = pd.DataFrame({'cat_col': ['A', 'B', 'C', 'D']})
test_df['cat_encoded'] = test_df['cat_col'].map(mean_target).fillna(0.5)
print(test_df['cat_encoded'].tolist())

medium

A. [1.0, 0.0, 0.0, 0.5]

B. [1.0, 0.0, 0.0, 0.0]

C. [1.0, 0.0, 0.0, NaN]

D. [0.5, 0.5, 0.5, 0.5]

Solution

1. Step 1: Calculate mean target per category from training data

   'A' has targets [1,1] mean=1.0, 'B' has [0] mean=0.0, 'C' has [0] mean=0.0.

2. Step 2: Map test categories and fill missing

   Test categories 'A','B','C' map to 1.0,0.0,0.0 respectively. 'D' is missing, so fillna(0.5) sets it to 0.5.

3. Final Answer:

   [1.0, 0.0, 0.0, 0.5] -> Option A

4. Quick Check:

   Map known means, fill unknown with 0.5 [OK]

Hint: Fill missing categories with default value after mapping [OK]

Common Mistakes:

• Not filling missing categories, resulting in NaN
• Using overall mean instead of per-category mean
• Miscomputing mean target values

4. You applied target encoding on your training data and then directly applied the same encoding on test data using the training means. However, your model shows signs of overfitting. What is the most likely mistake?

medium

A. You replaced missing values with zero instead of the mean

B. You did not normalize the target variable before encoding

C. You used target encoding on the entire dataset before splitting into train and test

D. You used one-hot encoding instead of target encoding

Solution

1. Step 1: Understand overfitting cause in target encoding

   Overfitting often happens if target encoding uses information from the test set or entire data before splitting.

2. Step 2: Identify mistake in data leakage

   Encoding before splitting leaks target info from test data into training, causing overfitting. Other options do not explain this leakage.

3. Final Answer:

   You used target encoding on the entire dataset before splitting into train and test -> Option C

4. Quick Check:

   Encoding before split causes data leakage [OK]

Hint: Always fit encoding only on training data to avoid leakage [OK]

Common Mistakes:

• Encoding before train-test split causing leakage
• Confusing normalization with encoding
• Ignoring missing value handling

5. You have a categorical feature with many rare categories in your training data. How can you apply target encoding to reduce overfitting caused by these rare categories?

hard

A. Use one-hot encoding instead of target encoding for rare categories

B. Use smoothing by combining category mean with overall mean weighted by category frequency

C. Apply target encoding only on the most frequent category and ignore others

D. Replace rare categories with a fixed constant before encoding

Solution

1. Step 1: Understand overfitting from rare categories

   Rare categories have few samples, so their target mean can be noisy and cause overfitting.

2. Step 2: Apply smoothing to reduce noise

   Smoothing blends the category mean with the overall mean, weighted by how many samples the category has, reducing noise for rare categories.

3. Final Answer:

   Use smoothing by combining category mean with overall mean weighted by category frequency -> Option B

4. Quick Check:

   Smoothing balances rare category means with global mean [OK]

Hint: Smooth rare categories by mixing with overall mean [OK]

Common Mistakes:

• Ignoring rare categories causing noisy means
• Replacing rare categories with constants losing info
• Using one-hot encoding which increases dimensionality