Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples
This addresses the lack of quantitative input-output relationships in AI, offering a method to enhance model performance in imbalanced classification tasks, though it is incremental as it builds on existing ensemble techniques.
The study tackled the 'black box' problem in AI by analyzing 323 models predicting human essential proteins, uncovering a ratio law linking model performance to sample ratios and proving optimal performance on balanced datasets, with an equation-guided ensemble strategy improving performance by 4.06% and 5.28% over traditional methods.
Artificial intelligence based on machine learning and deep learning has made significant advances in various fields such as protein structure prediction and climate modeling. However, a central challenge remains: the "black box" nature of AI, where precise quantitative relationships between inputs and outputs are often lacking. Here, by analyzing 323 AI models trained to predict human essential proteins, we uncovered a ratio law showing that model performance and the ratio of minority to majority samples can be closely linked by two concise equations. Moreover, we mathematically proved that an AI model achieves its optimal performance on a balanced dataset. More importantly, we next explore whether this finding can further guide us to enhance AI models' performance. Therefore, we divided the imbalanced dataset into several balanced subsets to train base classifiers, and then applied a bagging-based ensemble learning strategy to combine these base models. As a result, the equation-guided strategy substantially improved model performance, with increases of 4.06% and 5.28%, respectively, outperforming traditional dataset balancing techniques. Finally, we confirmed the broad applicability and generalization of these equations using different types of classifiers and 10 additional, diverse binary classification tasks. In summary, this study reveals two equations precisely linking AI's input and output, which could be helpful for unboxing the mysterious "black box" of AI.