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Overfitting occurs when a statistical model fits a particular data set too closely. This phenomenon is the main problem of machine learning, which prevents the generalization of models (Provost and Fawcett, 2013). In other words, in machine learning, a model learns patterns specific to a particular training data set that are irrelevant to other sets. The main consequence of overfitting is a decrease in predicting accuracy. Thus, the main method for detecting this phenomenon, which is used in my practice, is reviewing validating metrics, including accuracy and loss (Bejani and Ghatee, 2021). At the same time, overfitting detection is impossible without data testing. Thus, another method for detecting this phenomenon is to divide the dataset into testing and training subsets (Ying, 2019). While the training set represents most of the data, the testing set is used to test accuracy by measuring performance separately in the two separate parts of the data set. With these approaches, my organization uses machine learning more effectively by generalizing data more accurately, which increases its overall performance.
First of all, working with overfitting and its detection methods allow me to develop professionally as a banker. With the expansion of my experience with datasets and machine learning, I can more effectively manage the generalization of models, which makes them more efficient. In terms of personal development, this opens up great opportunities for me to continue my studies in the framework of machine learning. In general, overfitting detection improves the data generalization process, which improves the performance of models and the accuracy of predictions (Carmona et al., 2019). Thus, as a banker, mastering detection methods and overcoming overfitting allow me to contribute to the development of more efficient deep learning models within banking systems.
Bias-variance trade-offs are another property of models that affects their accuracy. The classical bias-variance trade-off suggests that variance increases and bias decreases as model complexity increases (Yang et al., 2020). Bias leads to oversimplification of the model and a high number of errors in both training and test data sets (Belkin et al., 2019). Variance prevents the generalization of data to unfamiliar sets, which leads to errors in test sets (Belkin et al., 2019). The interaction of these parameters results in overfitting (low bias-high variance) and underfitting (high bias-low variance). Thus, to improve the accuracy of the model, it is necessary to strike a balance between overfitting and underfitting, which results in d low bias and low variance.
As part of the practice of my banking organization, the bias-variance trade-off was considered primarily through the review of metrics, including the number of errors in the training data set. Another example of looking at bias-variance trade-off was identifying errors in a testing data set. The identification of these phenomena has influenced banking organizations’ prediction models primarily through the need to create more complex models in order to reduce the number of errors. Another influence of bias-variance trade-off on prediction models is the resizing of testing and training datasets to find a balance between two variables.
The described examples allowed the organization to improve the accuracy of the analysis and generalization of financial data, which led to an overall increase in inaccuracy. In the context of organizational and personal decision making, consideration of the phenomenon of bias-variance trade-off allowed taking into account the presence of errors both in training and in testing data sets. Such data helps in correcting decision-making taking into account possible errors.
Reference List
Bejani, M. M. and Ghatee, M. (2021) ‘A systematic review on overfitting control in shallow and deep neural networks, Artificial Intelligence Review, 54, pp. 6391-6438. Web.
Belkin, M., Hsu, D., Ma, S. and Mandal, S. (2019) ‘Reconciling modern machine-learning practice and the classical bias-variance trade-off’, PNAS, 116(32), pp. 15849-15854. Web.
Carmona, P., Climent, F. and Momparler, A. (2019) ‘Predicting failure in the U.S. banking sector: an extreme gradient boosting approach’, International Review of Economics & Finance, 61, pp. 304-323. Web.
Provost, F. and Rawcett, T. (2013) Data sciences for business: What you need to know about data mining and data-analytics thinking. O’Reilly.
Yang, Z., Yu, Y., You, C., Steinhardt, J. and Ma, Y. (2020) ‘Rethinking bias-variance trade-off for generalization of neural networks,Proceedings of the 37th International Conference on Machine Learning, pp. 1-11. Web.
Ying, X. (2019) ‘An overview of overfitting and its solutions’, Journal of Physics: Conference Series, 1168(2), pp. 1-6. Web.
Do you need this or any other assignment done for you from scratch?
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