Machine Learning: Bias and Variance

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There are critical points in machine learning that may compromise a prediction model. The relation between bias, variances, and learning models requires a careful examination of data sets used for training (Provost and Fawcett, 2013). This paper aims to assess the impact of bias and variances on prediction models and discuss three ways in which the behavior of such frameworks is adjusted to accommodate their influence.

Model prediction can provide highly valuable insights into many real-life situations. However, hidden patterns that are revealed by machine analysis require extrapolating data that may not explicitly copy the examples on which such frameworks were created (Rocks and Mehta, 2022). Therefore, there is a direct relation between bias, variances, and the efficiency of the prediction models. High levels of bias lead to models that are fast to generate yet underfitting, implying that the data is not represented correctly (Brand, Koch, and Xu, 2020; Botvinick et al., 2019). High variance can be similarly detrimental for a prediction, as a model trained on a highly specific data cluster will be able to predict outcomes that are too complex for utilizing outside of the example set (Brand, Koch, and Xu, 2020; Knox, 2018). Optimization of a prediction model can be achieved by utilizing overparameterized sets that can be later ‘trimmed’ for less global methods (Belkin et al., 2019). It is paramount to decide on the desired level of generalizability of a learning model prior to setting maximum acceptable bias and variance.

The trade-off in such cases requires one to sacrifice either applicability or accuracy in order to find a suitable level of complexity for a model. The optimal performance of a learning model can only be achieved by minimizing the total error (Singh, 2018). The three states of a prediction model are either too complex, too simple, or a perfect fit (Kadi, 2021). The goals of a model must define complexity, as leaving decisions to an improperly trained model may severely impact a firm’s performance (Delua, 2021). Traditional machine learning methods require finding a sufficient level of generalization at the cost of functional losses (McAfee and Brynjolfsson, 2012; Yang et al., 2020). In real life, any implementation of a statistical predictor is linked with margins for error that must be acceptable for the given situation. For example, IBM’s AI-powered cancer treatment advisor Watson was giving incorrect suggestions due to high bias (Mumtaz, 2020). The detrimental impact of such a learning model is apparent in its potential for harm.

In conclusion, an efficient prediction model requires its creators to find a balance between bias and variances to remain applicable in practice. Oversimplification or overfitting can lead to errors in predictions to the point of turning an algorithm unusable in real life. The trade-off in accuracy is required for a learning model to remain applicable, yet such a decision must be based on a practical implication.

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.

Botvinick, M. et al. (2019) ‘Reinforcement learning, fast and slow’, Trends in Cognitive Sciences, 23(5), pp. 408–422.

Brand, J., Koch, B. and Xu, J. (2020) Machine learning. London, UK: SAGE Publications Ltd.

Delua, J. (2021) Supervised vs. unsupervised learning: What’s the difference?, IBM. Web.

Kadi, J. (2021) The Relationship Between Bias, Variance, Overfitting & Generalisation in Machine Learning Models, Towards Data Science. Web.

Knox, S.W. (2018) Machine learning: A concise introduction. Hoboken, NJ: John Wiley & Sons, Inc.

McAfee, A. and Brynjolfsson, E. (2012) Big data: The management revolution, Harvard Business Review. Web.

Mumtaz, A. (2020) How to incorporate bias in your predictive models, Towards Data Science. Web.

Provost, F. and Fawcett, T. (2013) Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O’Reilly.

Rocks, J.W. and Mehta, P. (2022) ‘Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models’, Physical Review Research, 4(1).

Singh, S. (2018) Understanding the Bias-Variance Tradeoff, Towards Data Science. Web.

Yang, Z. et al. (2020) ‘Rethinking bias-variance trade-off for generalization of neural networks’, in Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research. Web.

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Machine Learning: Bias and Variance

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