Which is simply the average target value for samples of the same category as xⁱ of sample k, smoothed by some prior p, with weight a > 0. We want to substitute it with an estimate of

More formally, let’s consider the category i of the k-th training example. In general, the encoded quantity is an estimation of the expected target value in each category of the feature. Processing of Categorical Variables in CatBoost Ordered Target Statistics ExplainedĬatBoost proposes an inventive method for processing categorical features, based on a well-known preprocessing strategy called target encoding. This has the effect of multiplying the number of features that are scanned by the algorithm at each split, and that is why libraries such as CatBoost and LightGBM implement more scalable methods. For a given variable, the method creates a new column for each of the categories it contains. When implementations do not support categorical variables natively, as is the case for XGBoost and HistGradientBoosting, one-hot encoding is commonly used as a standard preprocessing technique.

For the XGBoost adepts, we show how to leverage its sparsity-aware feature to deal with categorical features. We demonstrate that LightGBM’s native categorical feature handling makes training much faster, resulting in a 4 fold speedup in our experiments. We first explain CatBoost’s approach for tackling the prediction shift that results from mean target encoding. We’ll now have a closer look at the way categorical variables are handled by LightGBM and CatBoost.

Previously, we investigated the differences between versions of the gradient boosting algorithm regarding tree-building strategies.

How to do one hot encoding in r