论文标题
PYSTACKED：在Stata中堆叠概括和机器学习
pystacked: Stacking generalization and machine learning in Stata
论文作者
论文摘要
PyStacked通过Python的Scikit-Learn实施了堆积的概括（Wolpert，1992），以进行回归和二进制分类。堆叠将多个监督的机器学习者（“基础”或“ 0级学习者”）结合到一个学习者中。当前支持的基础学习者包括正规化回归，随机森林，梯度增强的树木，支撑矢量机和前馈神经网（多层感知器）。 PyStacked也可以用作“常规”机器学习程序，以适合单个基础学习者，因此为Scikit-Learn的机器学习算法提供了易于使用的API。
pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-learn. Stacking combines multiple supervised machine learners -- the "base" or "level-0" learners -- into a single learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multi-layer perceptron). pystacked can also be used with as a `regular' machine learning program to fit a single base learner and, thus, provides an easy-to-use API for scikit-learn's machine learning algorithms.
