论文标题
粗略学习者的非参数概率回归
Nonparametric Probabilistic Regression with Coarse Learners
论文作者
论文摘要
概率回归是指在特征上预测目标条件的完全概率密度函数。我们提出了针对此问题的非参数方法，该方法结合了对目标价值的不同粗略训练的基础分类器（通常是梯度增强的森林）。通过将这种分类器结合并平均产生的密度，我们能够计算精确的条件密度，对密度的形状或形式的假设最少。我们将这种方法与结构化的跨透明损失函数相结合，该函数可将所得密度正常和平滑。从这些密度计算出的预测间隔在实践中显示具有高忠诚。此外，检查这些密度在特定观察结果上的特性可以提供有价值的见解。我们在各种数据集上演示了这种方法，并显示了竞争性能，尤其是在较大的数据集中。
Probabilistic Regression refers to predicting a full probability density function for the target conditional on the features. We present a nonparametric approach to this problem which combines base classifiers (typically gradient boosted forests) trained on different coarsenings of the target value. By combining such classifiers and averaging the resulting densities, we are able to compute precise conditional densities with minimal assumptions on the shape or form of the density. We combine this approach with a structured cross-entropy loss function which serves to regularize and smooth the resulting densities. Prediction intervals computed from these densities are shown to have high fidelity in practice. Furthermore, examining the properties of these densities on particular observations can provide valuable insight. We demonstrate this approach on a variety of datasets and show competitive performance, particularly on larger datasets.
