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.
Community And Workplace Intervention Programs
Introduction
The best method of drug usage management is by educating people about the risks that drugs pose to their health and the health of society. One can use many efficient drug addiction control strategies in several contexts and with various areas of concentration (Zastrow, Kirst-Ashman & Hessenauer, 2019). This paper will discuss drug intervention programs integrated into the workplace and society and provide drug addiction management for a person or a group of teenagers. Intervention programs can also include concentrating more on environmental and social characteristics, practices, early life experiences, or training programs.
Community Initiatives
A community presents a variety of possible preventative objectives and is frequently thought of as an all-encompassing, close-by system that gives resources and opportunities that influence individuals’ lifestyles. Society strongly emphasizes social engagement and frequently pays attention to young people in high-risk locations (Zastrow, Kirst-Ashman & Hessenauer, 2019). Drug preventive programs in the community may also use outside support, such as the press and nearby companies, to help control drug consumption. Justifications for setting up community-based preventative initiatives, drug training, and control initiatives can be contentious. Still, they often gain more support from the community when they incorporate a diverse group of people.
Workplace Programs
An effective plan must have a documented drug-free company policy as its foundation. Random drug testing is a safeguard for the company that can stop workers from reporting to the workplace when they are not fit to do their jobs (Zastrow, Kirst-Ashman & Hessenauer, 2019). The company can consider if random testing is necessary for the staff as the first thing to think about before putting it in place. The ultimate effectiveness of workplace initiatives will depend on how the company introduces and communicates the drug-free guidelines to the personnel and how to educate them about problems associated with drug consumption. Management may require an employee assistance program if a sizable section of the firm’s workforce is also in danger from social and emotional issues.
Reference
Zastrow, C., Kirst-Ashman, K. K., & Hessenauer, S. L. (2019). Empowerment series: understanding human behavior and the social environment. Cengage Learning.
Analysis Of Amazon Financial Reports For 3 Years
2019
Amazon did not experience any shocks in 2019, demonstrating normal development. With $280,522 in annualized recorded revenue in 2019, Amazon showed no big jump compared with 2018 (Amazon, 2020). Massive movements can be seen in the area of sustainable development and research. One of the principal investment areas was funding for solving climate problems. There are no significant changes in assets and equity accounts; the company slightly increased its profit compared to the previous year. As in previous periods, losses do not exceed profits and are within normal limits (Amazon, 2020). By December 2019, Amazon’s debt-to-equity ratio was 2.63, indicating that the company is successfully balancing its own and investment funds.
The current ratio for the period of interest is 1.10, which does not differ much from previous periods, indicating financial stability. The quick ratio is 0.83, demonstrating that the company is liquid and safe to invest in. The return on equity in 2019 amounted to approximately 21 percent, which is an average value for developing companies, and development is proceeding profitably. The net profit margin is slightly more than 4 percent, indicating that the company still has space to grow.
2020
2020 has been a shocking year for the world, and Amazon is no exception. However, the annual income for this period increased dramatically compared to all previous periods and amounts to $386,064 (Amazon, 2021). This year, the company is paying particular attention to ensuring the safety of employees, and the most significant movement of funds can be seen in the measures to combat COVID-19 (Amazon, 2021). There is a significant increase in assets and equity accounts; the company begins to increase profits sharply. The company’s losses this year are minimal; there is positive growth in a percentage ratio of more than 80% (Amazon, 2021). By December 2020, the debt-to-equity ratio was 2.63, corresponding to the previous period, and the balance is preserved. The current ratio dropped to 1.05, also a characteristic indicator showing that stability is maintained. The quick ratio has not changed much; liquidity has been preserved. Return on equity raised to about 27%, indicating an increase in profitability. The net profit margin has increased by about a percentage point, which is not a bad change, but there is still room for future development.
2021
2021 at Amazon was a period of trying to maintain pandemic growth. The organization succeeded in doing so: the reported annual income amounted to almost $470,000 (Amazon, 2022). However, it was not possible to achieve the same high indicators in terms of the ratio of profit and loss (Amazon, 2022). The most significant movement of funds can again be seen in the area of development and innovation. The debt-to-equity ratio fell to 2.04, but this is not such a sharp drop; the company is successfully coping with difficulties. The current ratio rose to 1.14, showing an increase in financial soundness. The quick ratio and return on equity remained almost unchanged with pandemic indicators; the company maintains its resilience. Net profit margin rose to 7 percent – a significant breakthrough; the company is developing rapidly and becoming more attractive to investors.
Summary
Amazon has shown steady growth over the past three years. Pre-pandemic 2019 is characterized by a slight profit increase and stable preservation of other indicators. The sharp jump in growth in 2020 was due to the demand for the service during the COVID-19 pandemic. Nevertheless, in 2021, Amazon managed to maintain and increase its performance, indicating the correct management. Amazon can be called an attractive company to invest in, as it successfully copes with complexities, is clean in its financial statements, and is overgrowing.
References
Amazon. (2020). Annual Report 2019.
Amazon. (2021). Annual Report 2020.
Amazon. (2022). Annual Report 2021. Web.