staff Target encoding is a powerful feature engineering technique, but there are better tools for some jobs. Experimenting with different methods and finding the one that best suits your needs is essential.
This method is beneficial for features with a natural ordering or many categories, which can be difficult for other encoding techniques to handle. However, it also has some limitations.
Reduces the Complexity of Categorical Variables
Categorical variables often contain a lot of information. Understanding their relationships can be challenging, making it difficult for models to predict accurately.
However, using techniques like target encoding, categorical features can be reduced in size. It involves replacing each category with its average value, reducing the number of categories in a feature, and making it easier for models to predict.
One drawback to target encoding is that it can cause overfitting, which occurs when the model learns its training data too well and performs poorly on new, unseen data. However, this can be overcome using methods that prevent overfitting, such as one-hot or label encoding.
Another benefit of target encoding is that it works well with high cardinality features. It is useful when trying to model data with many categories, which can be challenging for other encoding methods. For example, you have a feature with categories such as “purpose of loan” (debt consolidation, credit card, home improvement, etc.) and want to build a model that can predict credit risk.
Can Show Important Patterns
Categorical data often contains essential patterns that can help improve model performance. These patterns might be a natural ordering of categories (like low, middle, and high) or a specific frequency of category occurrences. While other encoding methods, like one-hot encoding, can miss these important patterns, target encoding can make them more visible.
However, it’s important to note that the encoded feature still depends on the target variable and can cause data leakage if not used carefully. It is because the model may have access to information it shouldn’t and could lead to overly optimistic performance estimates. To avoid this, it’s essential to use cross-validation and not just train on the training set.
It is particularly crucial for tree-based models that can handle categorical data without encoding. However, non-tree-based models such as linear regression require numerical values and can benefit from encoding.
To minimize the risk of overfitting, consider using James-Stein encoding when performing target encoding. This technique uses shrunk estimates of the target mean for each category to reduce the potential for overfitting.
Can Be Used with High Cardinality Features
Data science commonly uses categorical features, but ML models must be built to handle them well. Using flat features without a proper transformation can lead to overfitting or the model learning the patterns in the training set and then overestimating its performance when applied to new data.
Luckily, some methods help deal with this problem. One is called target encoding, which transforms categorical features into numerical ones. It works exceptionally well for features with high cardinality, i.e., features with many categories.
For example, suppose you have a feature like a car model with many different types. In that case, you can replace it with the average accident rate to simplify your model without losing important information. It can make the model more accurate and save you money on your insurance.
However, relying on an average value may only be helpful in some cases. For example, calculating an average will miss that relationship if the categories have a natural ordering.
Can Be Used with Other Encoding Methods
In our studies on the interaction of encoding methods with different ML algorithms, we found that using target encoding on categorical features enhanced their performance. This was especially true for tree-based models. However, non-tree-based models such as linear regression and neural networks also need numerical data to perform their mathematical operations. Hence, they are good candidates for encoding as well.
For example, a bank might use ML to predict the credit risk of loan applicants. One feature they should include in this prediction is why the applicant is applying for a particular loan. For this feature, it can transform the categories into numbers, such as the average purchase rate for each city.
It can help the model understand the categories better and predict them more accurately. It is something that other encoding methods, such as one-hot encoding, may need help with.
