Modern technology development has led to the appearance of a massive amount of information. As a result, big data has emerged, based on which various decisions can be made (McAfee and Brynjolfsson, 2012). Data science has appeared, dealing with models that process information (Provost and Fawcett, 2013). However, although modern technologies can process vast amounts of data, trying to fit models perfectly can lead to overfitting. This essay aims to analyze this problem and answer the question of whether overfitting is a general problem for all models or not.
This issue is characterized by a model’s perfect match with the training set. As a result, the system cannot adequately perceive other data. This phenomenon has its greatest prevalence in supervised learning due to the use of labeled datasets (Delua, 2021). According to the classical approach to machine learning, overfitting is a significant problem. First, if the model’s predictions match the training set ideally, there is a chance that the model captures data noise, which always exists (Bilbao and Bilbao, 2017). Secondly, the propensity for overfitting makes it challenging to use complex relationships, as in the case of deep neural networks, since this phenomenon requires training data to be limited (Srivastava et al., 2014). Finally, the overfitting process creates an overly optimistic impression of model performance due to artificially relevant results (Steyerberg, 2019). These factors can appear in any model; therefore, overfitting should always be considered.
However, several examples prove the opposite and allow for overfitting. First, its current understanding corresponds to its classical negative definition. There are examples of neural networks that work perfectly even on test data, although they fall under the concept of overfitting (Belkin et al., 2019). Another example is the existence of Automated Program Repair systems that fix bugs in software by creating patches that overfit as a side effect (Le et al., 2018). However, this does not significantly affect their performance and efficiency. Finally, it is worth noting that these systems, despite their tendency to overfitting, do not impair software performance and perform better than novice programmers (Smith et al., 2015). Therefore, in some cases, the use of this phenomenon may be valid.
However, overfitting is closely related to a bias-variances tradeoff since it is connected to keeping bias and variance low while maintaining sufficient precision. When maximizing the data accuracy, the variance is increased accordingly, making the model irrelevant in the real world. An example of this behavior is YOGI’s U-model of operation, a verification engine that works best at a sufficiently high but not maximum “i” value (Sharma, Nori and Aiken, 2014). Therefore, overfitting refers to the variance part of the indicated balance sought in all models, making it necessary to address this phenomenon in all conditions.
Reference List
Belkin, M. et al. (2019) ‘Reconciling modern machine-learning practice and the classical bias–variance trade-off’, Proceedings of the National Academy of Sciences, 116(32), pp. 15849-15854.
Bilbao, I. and Bilbao, J. (2017). ‘Overfitting problem and the over-training in the era of data: particularly for Artificial Neural Networks’. Proceedings of the 8th international conference on intelligent computing and information systems (ICICIS). Cairo, Egypt.
Delua, J. (2021) ‘Supervised vs. unsupervised learning: what’s the difference?’. IBM, 12 March.
Le, X.B.D., et al. (2018). ‘Overfitting in semantics-based automated program repair’. Empirical Software Engineering, 23(5), pp. 3007-3033.
McAfee, A. and Brynjolfsson, E. (2012) ‘Big data: the management revolution’, Harvard Business Review, Web.
Provost, F. and Fawcett, T. (2013) Data science for business: what you need to know about data mining and data-analytic thinking. 1st edn. Sebastopol: O’Reilly Media.
Sharma, R., Nori, A. V., and Aiken, A. (2014). ‘Bias-variance tradeoffs in program analysis’. ACM SIGPLAN Notices, 49(1), pp. 127-137.
Smith, E.K. et al. (2015) ‘Is the cure worse than the disease? Overfitting in automated program repair’. Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, New York, United Stated.
Srivastava, N., et al. (2014) ‘Dropout: a simple way to prevent neural networks from overfitting’. The Journal of Machine Learning Research, 15(1), pp.1929-1958.
Steyerberg, E.W. (2019). Clinical prediction models. Cham: Springer.