Machine learning models fit complex algorithms to arbitrarily large datasets. These algorithms are well-known to be high on performance and low on interpretability. We use interactive visualization of slices of predictor space to address the interpretability deficit; in effect opening up the black-box of machine learning algorithms, for the purpose of interrogating, explaining, validating and comparing model fits. Slices are specified directly through interaction, or using various touring algorithms designed to visit high-occupancy sections or regions where the model fits have interesting properties. The methods presented here are implemented in the R package \pkg{condvis2}.

翻译：机器学习模型适合任意的大型数据集的复杂算法。 这些算法在性能和可解释性方面是众所周知的。 我们使用对预测空间片段的交互可视化来解决可解释性缺陷; 实际上打开机器学习算法的黑盒, 以便询问、 解释、 验证和比较模型。 切片直接通过互动或使用旨在访问该模型适合高占用区或区域的各种巡回算法来说明。 这里介绍的方法在 R 软件包\pkg{condvis2} 中实施 。