Similar target leakage also exists in standard gradient boosting algorithms. This leakage consequently increases the risk of overfitting on the training data, especially when the data is small. Target leakage means using some information from target to predict the target itself (you see the issue here?). In this particular post, I am focussing on one particular categorical encoding technique called target encoding which works really well most of the times, but it has a risk of target leakage if not done correctly. They keep on hiding the information until we transform them smartly. One type of features that do not easily give away the information they contain are categorical features. The goal of supervised learning is to extract all the juice from the relevant features and to do that, we generally have to enrich and transform features in order to make it easier for the algorithm to see how the target variable depends on given data. Jumping from simple algorithms to complex ones does not always boost performance if the feature engineering is not done right.
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