Boosting提升方法是一种常用的统计学习方法，应用十分广泛且有效。在分类问题中，它通过改变训练样本的权重，学习多个分类器，并将这些分类器进行线性组合，提高分类的性能。总而言之，提升算法基于这样一种思路：对于一个复杂任务来说，将多个专家的判断进行适当的综合所得出的判断，要比其中任何一个专家独断的判断好。最终三种Boost方法都在87%左右，最高87.6%。
2021-06-16 14:59:45
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陈天奇xgb论文。Tree boosting is a highly eective and widely used machine learning method. In this paper, we describe a scalable endto-
end tree boosting system called XGBoost, which is used
widely by data scientists to achieve state-of-the-art results
on many machine learning challenges. We propose a novel
sparsity-aware algorithm for sparse data and weighted quantile
sketch for approximate tree learning. More importantly,
we provide insights on cache access patterns, data compression
and sharding to build a scalable tree boosting system.
By combining these insights, XGBoost scales beyond billions
of examples using far fewer resources than existing systems.
2021-02-21 10:51:18
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