Target Encoding - Soulpage IT Solutions

What is Target Encoding? Target Encoding Explained

Target encoding, also known as mean encoding or likelihood encoding, is a technique used in machine learning and predictive modeling to encode categorical variables with the target variable’s mean or probability. It is commonly used in classification tasks where the target variable is categorical.

This encoding process involves replacing each category in a categorical variable with a numerical value that represents the relationship between that category and the target variable. The goal is to capture the target variable’s information within the encoded values, allowing the model to learn the associations between the categorical variable and the target more effectively.

Here’s a general overview of how target encoding works:

Data Preparation: Split the dataset into a training set and a validation or test set. Target encoding should be performed only on the training set to avoid data leakage.

Encoding Process: For each categorical variable, calculate the mean or probability of the target variable within each category in the training set. This can be done by grouping the data by the categorical variable and calculating the mean or probability of the target variable within each group.

Mapping: Create a mapping dictionary or table that associates each category in the categorical variable with its corresponding mean or probability value calculated in the previous step.

Encoding: Replace the original categorical values in both the training and validation/test sets with the corresponding mean or probability values from the mapping dictionary. This results in the categorical variable being transformed into a numerical variable.

Model Training: Train a machine learning model using the encoded features and the target variable.

Target encoding offers several benefits:

Utilizing Target Information: By encoding categorical variables with target-related information, this encoding technique can capture the relationship between the categorical variable and the target, potentially improving the model’s predictive performance.

Handling High Cardinality: It can be particularly useful when dealing with categorical variables with high cardinality (a large number of unique categories), as it condenses the information into a numerical representation without expanding the feature space.

However, there are some considerations when using target encoding:

Data Size: It requires a sufficiently large training dataset to obtain robust estimates of the target statistics for each category. It may lead to overfitting if the categories have too few samples or if the dataset is small.

Handling Unseen Categories: This technique requires handling unseen categories that are not present in the training set. One common approach is to assign a default value, such as the overall mean or a small constant, to unseen categories during encoding.

Regularization: To avoid overfitting, regularization techniques such as smoothing or adding noise to the encoded values can be applied. This helps prevent extreme values and reduces the impact of rare categories.

Target encoding is a powerful technique for encoding categorical variables by leveraging the target variable’s information. It can be an effective way to improve model performance in classification tasks. However, careful validation and regularization are necessary to ensure its proper application and prevent potential overfitting.