The spread of communication technologies has given rise to a significant increase in information volume. This has led to the emergence of the big data phenomenon and related analytic models (McAfee and Brynjolfsson, 2012). Most of them are considered within the data science, which actively uses machine learning to solve various problems (Provost and Fawcett, 2013). However, no matter how advanced learning algorithms are, researchers still face many issues. In particular, a significant concern within supervised machine learning is the bias-variance tradeoff (Delua, 2021). These two parameters are inextricably linked, and an increase in one leads to a decrease in the other, which threatens to go to the extreme – a state of under or overfitting (Briscoe and Feldman, 2011). Finding the right balance between them is the key to creating a quality model with the required parameters. This essay aims to analyze and evaluate this tradeoff, its subjectivity, and the relationship to the accuracy and reliability of the model.
In machine learning, bias is related to the accuracy of predictions based on initial data. The model may ignore the test information with high bias, causing significant errors (Singh, 2018). On the other hand, variance is responsible for the final data’s diversity. However, a model with too much variance begins to capture noise, leading to errors (Singh, 2018). Thus, the ideal state is low values of both considered parameters. However, they cannot be predetermined and can be discovered only by applying particular test metrics (What is the difference between bias and variance, n.d.). In addition, the final model error is also affected by external factors that cannot be eliminated, such as data noise (Singh, 2018). Even though reaching the ideal position between bias and variance seems mathematically possible, there is no guarantee that the perfect tradeoff point will be achieved. Therefore, such a balance is subjective and is assessed based on the purpose of the model.
However, achieving the bias-variance tradeoff is still highly desirable. First of all, a well-balanced model demonstrates a sufficient level of reliability since it adequately responds to various changes. Therefore, the model should not be under or overfitted, which is possible only under conditions close to balanced (What is the difference between bias and variance, n.d.). In addition, the closer the program is to balance, the more accurately it matches the actual pattern between the data. If the variance is too high, the model will capture noise, breaking the pattern, and if the bias is high, it will ignore the existing distribution principles (Singh, 2018). The presence of a balance allows the model to derive an average result close to the ideal following the researcher’s needs. In addition, the better the balance, the better the principles of data classification (Merentitis, Debes, and Heremans, 2014). In this case, the model will use only the necessary data, discarding noise.
However, the actual balance is still largely subjective and may deviate from accepted canons. Neural networks, which are often formed in an overfitted position, can confirm the subjectivity of the tradeoff (Belkin et al., 2019). Thus, in some cases, the researcher may deliberately shift the balance to adapt an overparameterized model (Dar, Muthukumar, and Baraniuk, 2021). In addition, this balance can be generalized and simplified, for example, for application in other scientific fields where perfectly accurate results are not required (Doroudi, 2020). However, some studies and algorithms allow getting closer to the ideal cutting point.
In this case, the subjectivity of this process is significantly reduced due to automation. First of all, combining several tools, such as an incremental increase in accuracy and cross-validation, allows for reaching such a state (Sharma, Nori, and Aiken, 2014). In addition, according to Mittas and Angelis (2016), the use of visual analytics and ensemble techniques allows a researcher to take advantage of different balancing approaches, thus getting as close as possible to the ideal tradeoff point. However, these machine algorithms only bring the parameters closer to the mathematically perfect point, which may not coincide with the researcher’s needs. Therefore, from my perspective, finding the bias-variance balance is a subjective task.
Reference List
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Briscoe, E. and Feldman, J. (2011) ‘Conceptual complexity and the bias/variance tradeoff’, Cognition, 118(1), pp. 2-16.
Dar, Y., Muthukumar, V. and Baraniuk, R.G. (2021) ‘A farewell to the bias-variance tradeoff? An overview of the theory of overparameterized machine learning’, arXiv.
Delua, J. (2021) ‘Supervised vs. unsupervised learning: what’s the difference?’. IBM.
Doroudi, S. (2020) ‘The bias-variance tradeoff: how data science can inform educational debates’, AERA Open, 6(4), p. 1-18.
McAfee, A. and Brynjolfsson, E. (2012) ‘Big data: the management revolution’, Harvard Business Review. Web.
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