Gradient Boosted Trees for Bike Sharing Data Prediction

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这篇博客关于 GBDT 的优缺点说的很清楚：
Different kinds of models have different advantages. The boosted trees model is very good at handling tabular data with numerical features, or categorical features with fewer than hundreds of categories.
One important note is that tree based models are not designed to work with very sparse features. When dealing with sparse input data (e.g. categorical features with large dimension), we can either pre-process the sparse features to generate numerical statistics, or switch to a linear model, which is better suited for such scenarios.