论文标题
Forestprune：紧凑的深度控制树合奏
ForestPrune: Compact Depth-Controlled Tree Ensembles
论文作者
论文摘要
树的合奏是实现出色预测性能的强大模型，但可以成长为笨拙的大小。这些合奏通常经过后处理（修剪），以减少记忆足迹并提高解释性。我们介绍了Forestprune，这是一个新颖的优化框架，通过修剪各个树木的深度层来对后处理树的合奏。由于决策树中的节点的数量随树深而成倍增加，因此修剪深树会极大地压实合奏。我们开发了一种专门的优化算法，以有效地获得森林中问题的高质量解决方案。我们的算法通常在中型数据集和合奏中以10000秒和100棵树的形式在几秒钟内达到良好的解决方案，从而对现有方法产生了重大加速。我们的实验表明，ForestPrune产生的模型效果超过了现有后处理算法提取的模型。
Tree ensembles are powerful models that achieve excellent predictive performances, but can grow to unwieldy sizes. These ensembles are often post-processed (pruned) to reduce memory footprint and improve interpretability. We present ForestPrune, a novel optimization framework to post-process tree ensembles by pruning depth layers from individual trees. Since the number of nodes in a decision tree increases exponentially with tree depth, pruning deep trees drastically compactifies ensembles. We develop a specialized optimization algorithm to efficiently obtain high-quality solutions to problems under ForestPrune. Our algorithm typically reaches good solutions in seconds for medium-size datasets and ensembles, with 10000s of rows and 100s of trees, resulting in significant speedups over existing approaches. Our experiments demonstrate that ForestPrune produces parsimonious models that outperform models extracted by existing post-processing algorithms.
