What is Target Encoding in Machine Learning?
Target encoding, often called mean encoding, is used in machine learning for categorical variables. Target encoding converts categorical variables into numerical values with useful information for predictive modeling, especially when the variable includes multiple categories. This encoding approach replaces categorical features with category mean target variable. It is popular for regression and classification.
Target encoding is advantageous for high-cardinality features with many unique values. This strategy improves model performance by helping the machine learning algorithm capture the feature-target variable association.
Target encoding’s approach, merits, drawbacks, and common considerations will be examined in this article.
Understanding Target Encoding
Many machine learning datasets need categorical characteristics. However, most machine learning methods, notably mathematical models like linear regression, SVMs, and neural networks, use numerical input. Before being employed in a machine learning model, categorical data must be numerically converted. Traditional categorical variable encoding methods like one-hot and label have strengths and cons.
Target encoding improves existing methods by encoding categories by their link to the target variable. Target encoding replaces a categorical characteristic with the target variable mean for each category. The target’s mean value for each category is assumed to be predictive.
For example, if we have a categorical feature “City” with values like “New York,” “Los Angeles,” and “Chicago,” target encoding will replace each city with its average target variable (such as property prices or sales statistics). This lets the model learn the link between cities and the target variable, which is beneficial when there are many categories and one-hot encoding would generate a sparse matrix.
The Role of Target Encoding in Machine Learning Models
Target encoding captures the statistical link between the feature and the target to improve feature prediction. This is useful for decision trees, gradient boosting machines (GBMs), and other tree-based algorithms that forecast features-target variables connections.
Target encoding allows the model to examine the target variable’s distribution in respect to categorical values rather than treating each category as an independent feature, which may improve model performance. For categorical variables like “Region” or “Product Type,” target encoding may reveal patterns of higher or lower target values.
Target encoding helps machine learning models derive more meaningful correlations from categorical information. This improves generalization on unseen data, especially for categorical variables with numerous unique categories (high-cardinality features).
When to use Target Encoding?
High Cardinality Features: Very helpful for categorical features that have a lot of different values.
Regression and Classification Tasks: Both classification and regression tasks can use this technique to encode categorical data according to the goal.
Benefits of Target Encoding
- Reduction in Dimensionality: Target encoding avoids creating numerous columns for each category, unlike one-hot encoding. This is very useful for high-cardinality features. Most columns of a sparse matrix produced by one-hot encoding are zeroes. However, target encoding creates one column for each categorical feature, reducing dataset dimensionality.
- Improved Model Performance: Target encoding improves model performance by capturing the connection between the target variable and the categorical feature. Model accuracy and performance can be enhanced with the use of tree-based techniques including gradient boosting models, decision trees, and random forests.
- Better Handling of High-Cardinality Features: High-cardinality features, which have many different values, might be difficult for machine learning models to handle. One-hot encoding cannot handle these features as well as target encoding. Target encoding decreases complexity and memory needs by keeping important information without adding several columns for each category.
- Non-linear Relationships: Target encoding captures the non-linear correlations between the categorical feature and the target variable, unlike label encoding, which assigns arbitrary number values to categories. This helps the model learn non-linear patterns.
Disadvantages of Target Encoding
Target encoding has benefits but potentially drawbacks:
- Overfitting Risk: Overfitting is a major risk with target encoding. If the model is shown the goal mean of each category directly, it may become too dependent on these encoded values and fail to generalize to new data. This is especially problematic if the target variable has large volatility or the model is not regularized.
- Data Leakage: Target encoding can leak data if done incorrectly. When outside data is used to train the model, data leakage causes unduly optimistic model evaluation performance. Target encoding the full dataset before separating it into training and testing sets usually causes this. In such circumstances, the model might accidentally access test set data, skewing results.
- Complexity in Cross-Validation: Target encoding must be handled carefully to calculate the target mean for each category only from the training fold, not the entire dataset. This prevents the model from unfairly accessing test data.
- Handling of Rare Categories: Target encoding may give incorrect target mean estimates for unusual categories. This can be troublesome if such categories affect predicted performance significantly. Smoothing encoded values reduces this issue, but it complicates the encoding procedure.
Practical Considerations
- Smoothing: When utilizing target encoding, smoothing is used to avoid overfitting. Smoothing adjusts encoded values toward the desired mean to mitigate unusual category sample size effects. Preventing excessive encoded values for categories with limited occurrences reduces overfitting.
- Handling of New Categories: In practice, target encoding must be done carefully when new, unseen categories occur in the test set or during deployment. To encode additional categories, use a default value (such as the target variable’s global mean) or imputation for missing or unknown categories.
- Use in Different Algorithms: Target encoding is useful for many machine learning models, however tree-based techniques can handle continuous features well. Target encoding may introduce non-linearities that contradict with linear models, therefore they may not benefit.
- Experimentation and Tuning: Target encoding, like any feature engineering technique, requires experimentation and tuning. It is important to test multiple encoding algorithms, including smoothing and rare category adjustments, and use cross-validation. Target encoding efficacy depends on dataset and task.
Conclusion
In machine learning, target encoding is useful for managing categorical variables with high-cardinality characteristics. Target encoding lets machine learning models find patterns that other encoding methods overlook by encoding categories based on the feature-target variable relationship. The approach must be regulated to avoid overfitting and data leaking. Target encoding improves model speed, especially for tree-based models, when handled properly. Target encoding should be chosen based on the dataset, model, and categorical feature difficulties.