Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large-scale Uncertainty Estimation and Its Application in Revenue
  Forecast of SMEs   The economic and banking importance of the small and medium enterprise (SME)
sector is well recognized in contemporary society. Business credit loans are
very important for the operation of SMEs, and the revenue is a key indicator of
credit limit management. Therefore, it is very beneficial to construct a
reliable revenue forecasting model. If the uncertainty of an enterprise's
revenue forecasting can be estimated, a more proper credit limit can be
granted. Natural gradient boosting approach, which estimates the uncertainty of
prediction by a multi-parameter boosting algorithm based on the natural
gradient. However, its original implementation is not easy to scale into big
data scenarios, and computationally expensive compared to state-of-the-art
tree-based models (such as XGBoost). In this paper, we propose a Scalable
Natural Gradient Boosting Machines that is simple to implement, readily
parallelizable, interpretable and yields high-quality predictive uncertainty
estimates. According to the characteristics of revenue distribution, we derive
an uncertainty quantification function. We demonstrate that our method can
distinguish between samples that are accurate and inaccurate on revenue
forecasting of SMEs. What's more, interpretability can be naturally obtained
from the model, satisfying the financial needs.

3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci