A method for explaining individual predictions in neural networks
Sejong Oh
TL;DR
This paper introduces a method to explain how neural networks make predictions by calculating the contribution of each input value.
Contribution
The novel contribution is a method to interpret neural network predictions by analyzing input weights and their influence on outputs.
Findings
The proposed method evaluates input contributions using weighted sums and network weights.
The method is applicable to both regression and classification models regardless of network depth.
A Python library was developed to implement the method, making it accessible for practical use.
Abstract
Recently, the explainability of the prediction results of machine learning models has attracted attention. Most high-performance prediction models are black boxes that cannot be explained. Artificial neural networks are also considered black box models. Although they can explain image classification results to some extent, they still struggle to explain the classification and regression results for tabular data. In this study, we explain the individual prediction results derived from a neural network-based prediction model. The output of a neural network is fundamentally determined by multiplying the input values by the network weights. In other words, the output is a weighted sum of the input values. The weights control how much each input value contributes to the output. The degree of influence of an input value xi on the output can be evaluated as (xi · weight value wi)/weighted…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Adversarial Robustness in Machine Learning · Statistical and Computational Modeling