Answer: Tree ensembles, unlike linear models, inherently handle categorical data without requiring one-hot encoding due to their split-based nature.

Tree ensembles, such as Random Forests and Gradient Boosting Machines (GBMs), have a unique capability to handle categorical data without the need for one-hot encoding. This ability stems from the fundamental way in which decision trees make splits during training.

In a decision tree, each node represents a feature and a split point, and the data is partitioned based on whether each data point satisfies the condition defined by that split. This process continues recursively, with the tree growing deeper until certain stopping criteria are met (e.g., maximum depth reached, minimum number of samples per leaf node).

When it comes to categorical features, decision trees simply compare the feature values directly against the different categories. For example, if a feature represents colors and has categories like “red,” “blue,” and “green,” the tree might split the data based on whether the color is “red” or not, then further split based on other categories if necessary. This way, the tree can naturally handle categorical variables without any preprocessing like one-hot encoding.

This handling of categorical variables in tree ensembles offers several advantages:

1. Simplicity: Tree ensembles simplify the preprocessing pipeline by eliminating the need for one-hot encoding, reducing the complexity and potential for errors in data preparation.
2. Efficiency: Since categorical variables are directly incorporated into the tree structure, there is no increase in the dimensionality of the dataset, which can lead to more efficient training and prediction processes, especially for large datasets.
3. Interactions: Decision trees inherently capture interactions between categorical variables as they partition the data based on these variables, allowing for more expressive models without the need for explicit feature engineering.
4. Robustness: Tree ensembles can handle missing values in categorical features gracefully, by treating them as a separate category or by choosing the split that minimizes impurity without considering missing values, which enhances robustness in real-world datasets.

However, it’s important to note that while tree ensembles can handle categorical variables without one-hot-encoding, they still require numerical encoding for ordinal categorical variables (e.g., “low,” “medium,” “high”). Additionally, the treatment of categorical variables in tree ensembles may vary slightly between different implementations and configurations, but the core principle remains the same: direct comparison of category values in the splitting process.