The structure of data organization is widely recognized as having a
substantial influence on the efficacy of machine learning algorithms,
particularly in binary classification tasks. Our research provides a
theoretical framework suggesting that the maximum potential of binary
classifiers on a given dataset is primarily constrained by the inherent
qualities of the data. Through both theoretical reasoning and empirical
examination, we employed standard objective functions, evaluative metrics, and
binary classifiers to arrive at two principal conclusions. Firstly, we show
that the theoretical upper bound of binary classification performance on actual
datasets can be theoretically attained. This upper boundary represents a
calculable equilibrium between the learning loss and the metric of evaluation.
Secondly, we have computed the precise upper bounds for three commonly used
evaluation metrics, uncovering a fundamental uniformity with our overarching
thesis: the upper bound is intricately linked to the dataset's characteristics,
independent of the classifier in use. Additionally, our subsequent analysis
uncovers a detailed relationship between the upper limit of performance and the
level of class overlap within the binary classification data. This relationship
is instrumental for pinpointing the most effective feature subsets for use in
feature engineering.

数据组织结构对机器学习算法的有效性有巨大影响，特别是对于二元分类任务。本研究提供了一个理论框架，表明给定数据集上二元分类器的最大潜力主要受到数据固有特性的限制。通过理论推理和实证检验，我们得出两个主要结论：首先，我们展示了在实际数据集上二元分类的性能的理论上界是可以被理论上达到的；这个上界代表了学习损失和评估指标之间的可计算平衡。其次，我们计算了三个常用评估指标的精确上界，揭示了与我们的总体论点的基本一致性：上界与数据集的特征紧密联系，与所使用的分类器无关。此外，我们的后续分析揭示了性能上限与二元分类数据中类别重叠水平之间的详细关系，这对于确定在特征工程中使用的最有效的特征子集至关重要。

二元分类性能中的内在数据约束和上限

Intrinsic Data Constraints and Upper Bounds in Binary Classification  Performance

Degradation models play an important role in Blind super-resolution (SR). The
classical degradation model, which mainly involves blur degradation, is too
simple to simulate real-world scenarios. The recently proposed practical
degradation model includes a full spectrum of degradation types, but only
considers complex cases that use all degradation types in the degradation
process, while ignoring many important corner cases that are common in the real
world. To address this problem, we propose a unified gated degradation model to
generate a broad set of degradation cases using a random gate controller. Based
on the gated degradation model, we propose simple baseline networks that can
effectively handle non-blind, classical, practical degradation cases as well as
many other corner cases. To fairly evaluate the performance of our baseline
networks against state-of-the-art methods and understand their limits, we
introduce the performance upper bound of an SR network for every degradation
type. Our empirical analysis shows that with the unified gated degradation
model, the proposed baselines can achieve much better performance than existing
methods in quantitative and qualitative results, which are close to the
performance upper bounds.

本文提出了一种统一的门控降级模型，根据该模型，提出了可以有效处理非盲，经典，实用降级案例以及许多其他边角案例的简单基线网络，并提出了每种降级类型的 SR 网络性能上限，实验证明，该模型可以更好地达到与性能上限接近的定量和定性结果。