Introduction
Decision trees are prone to several problems, including overfitting, overly complex information, high variance, and bias in outcomes, and it is essential to mitigate these issues.
Overfitting Overview
The most fundamental problem of tree induction is that all decision trees (DTs) are prone to overfitting. This concept refers to the situation when the framework teaches the model according to the existing data so thoroughly that the model has difficulties with the classification of new information (Provost and Fawcett, 2013). Thus, overfitting introduces two critical challenges to the effectiveness of DTs – poor performance of the model on new datasets and overload of the model with unnecessary information and noise (Birla, 2020). In other words, the framework tends to teach “too much” to the model, resulting in data saturation and associated issues.
To solve these problems, the best method is to find the exact spot of the learning process between under-fitting and over-fitting. One of the approaches to achieve this goal is data pruning (Yse, 2019). This concept implies the reduction of DT nodes that contribute little useful information and overload the dataset (Yse, 2019). The two primary approaches include pre-prune and post-prune (Kumar, 2021). The former concerns early stopping when the nodes of the tree start providing unreliable information and worsen the quality of the model (Ying, 2019). However, it might be challenging to cease the learning process at the desired spot, so many analysts utilize the post-prune method. This approach implies reducing the number of leaf nodes after the teaching cycle by carefully examining their impact on the model performance (Yse, 2019). Thus, if a specific node is found to overload the dataset, it needs to be removed to mitigate overfitting. Other methods of preventing overfitting include noise reduction in the training dataset, expansion of training information, and regularization (Ying, 2019). Ultimately, the goal of the approaches is to find the spot between underfitting and overfitting to mitigate the problems.
One example of overfitting in banking concerns strategies for fraud mitigation. At present, the banking sector encounters a solid quantity of fraud annually, but this number is not sufficient to train an efficient model (Winteregg, 2019). As a result, banks utilize periphery data that includes a lot of noise to teach their models, resulting in overfitting (Winteregg, 2019). Their frameworks get so accustomed to previous types of fraud that models cannot effectively deal with any innovative cybercrimes. Ultimately, banks need to collaborate to get more data about fraud and prepare functional training sets.
Critical Concerns with Decision Trees
Two other critical concerns with DTs include the inherently unstable nature of decision trees and the issues of bias-variance trade-offs. The former implies that DTs provide less stability than other machine learning frameworks, and even a small mistake might significantly change the outcome (‘Decision tree,’ n.d.). The second concern includes the balance between bias and variance in machine learning. DTs are generally low-bias/high-variance models, which leads to such problems as overfitting, complexity, and abundant data noise (Wickramasinghe, 2021).
Conclusion
Thus, similar to the problem of underfitting-overfitting, analysts need to find an appropriate balance between bias and variance to maximize the productivity of the model (Phoenix, 2022). It is a challenging task since an increase in bias inevitably leads to a decrease in variance, and it is essential to use innovative methods in machine learning and DTs to mitigate this issue. Ultimately, while decision trees have several drawbacks and concerns, they are highly effective models for classification and regression objectives.
Reference List
Birla, H. (2020). ‘Understanding decision trees’, Towards Data Science, Web.
‘Decision tree’ (n.d.).Web.
Kumar, S. (2021). ‘3 techniques to avoid overfitting of decision trees’, Towards Data Science, Web.
Phoenix, J. (2022). ‘Introduction to the bias-variance trade-off in machine learning’, Understanding Data, Web.
Provost, F. and Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytical thinking. California: O’Reilly.
Wickramasinghe, S. (2021). ‘Bias & variance in machine learning: Concepts & tutorials’, BMC, Web.
Winteregg, J. (2019). ‘How to overcome overfitting in machine learning based fraud mitigation for banks’, Net Guardians, Web.
Ying, X. (2019). ‘An overview of overfitting and its solutions’, Journal of Physics: Conference Series, 1168(2). Web.
Yse, D. L. (2019). ‘The complete guide to decision trees’, Towards Data Science, Web.