Most machine learning-based regressors extract information from data
collected via past observations of limited length to make predictions in the
future. Consequently, when input to these trained models is data with
significantly different statistical properties from data used for training,
there is no guarantee of accurate prediction. Consequently, using these models
on out-of-distribution input data may result in a completely different
predicted outcome from the desired one, which is not only erroneous but can
also be hazardous in some cases. Successful deployment of these machine
learning models in any system requires a detection system, which should be able
to distinguish between out-of-distribution and in-distribution data (i.e.
similar to training data). In this paper, we introduce a novel approach for
this detection process using a Reduced Robust Random Cut Forest (RRRCF) data
structure, which can be used on both small and large data sets. Similar to the
Robust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced
representation of the training data sub-space in form of cut trees. Empirical
results of this method on both low and high-dimensional data showed that
inference about data being in/out of training distribution can be made
efficiently and the model is easy to train with no difficult hyper-parameter
tuning. The paper discusses two different use-cases for testing and validating
results.

本文介绍了一种使用 Reduced Robust Random Cut Forest (RRRCF) 数据结构的新方法来检测数据集是否处于训练分布，该方法在低维和高维数据的实证结果表明可以有效地进行数据分布的推断，而且该模型易于训练，并且不需要进行困难的超参数调整，用例包括测试和验证结果。