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Introduction
In the organizational environment, making the most beneficial decisions is crucial for the future growth of the company. Currently, many executives use decision trees to formulate possible decision outcomes easily.
Advantages of Decision Trees
The most critical advantages of decision trees are their simplicity, specificity, and comprehensiveness. Simplicity is connected to the overall structure of these schemes, as they do not require sophisticated formulas to complete the necessary calculations (Singer and Cohen, 2020). When implementing decision trees for determining the best result, the executives can quickly create an overview of the potential choices, presenting them in a visual format and ensuring the simplicity of the decision-making procedure (Jaworski, Duda, and Rutkowski, 2018). Furthermore, given that decision trees display information visually, they are easy to explain to other employees and thus can be efficiently integrated into any presentation.
After that, the specificity of the decision trees is another benefit useful for organizational practice. In a company environment, quickly assessing a particular problem and identifying the factors related to it is exceptionally valuable, as overlooking some of the issues aspects can lead to a loss of profit or productivity (Provost and Fawcett, 2013). In this regard, decision trees are perfectly fit for avoiding such complications, as they allow ascertaining the problem and its context, as well as the available solution, using the decision tree scheme only.
Finally, not only do the decision trees consider the factual information, but they also provide an insight into the possible outcomes linked to each resolution pathway, covering probable development scenarios. This feature is highly useful when solving issues, the consequences of which can have a significant impact on the enterprises operations (Kumar and Garg, 2018). When approaching such problems, it is essential to address both current conditions and their ramifications (Yeo and Grant, 2018). Decision trees are an excellent instrument for analyzing such information and comparing the action pathways to choose the most suitable one.
The Roles of Entropy and Information Gain
Entropy and information gain are the primary attributes of decision trees utilized to find the most appropriate distribution of a given dataset. Although both entropy and information gain can be used to determine the best split of the nodes, these concepts play different roles in decision trees (Singer, Anuar, and Ben-Gal, 2020). As such, while entropy is the measure of the variables disorder or uncertainty, information gain refers to the amount of information about the class that can be received from a particular feature (Thomas, Vijayaraghavan, and Emmanuel, 2020). From this perspective, entropys role in decision trees is to identify the most suitable way of splitting the data while ensuring that the impurity of this information remains as low as possible (Gupta et al., 2017). On the other hand, information gain offers insight into how much knowledge was gained after a certain entropy was used to perform the split (Patel and Prajapati, 2018). Therefore, if entropy is necessary for specifying the uncertainty and decreasing it, information gain provides an overall perspective on how useful the split was based on the given entropy.
Conclusion
A perfect example to illustrate the role differences could be a scenario in which a company executive must decide how investing in a particular stock could increase or decrease the organizations liabilities and profits. In this case, entropy will be necessary to determine how to split the available variables, liabilities, and profits, based on the features, which would be the available stock options. In contrast, information gain will provide insight into how informative are the utilized features and whether they offer enough data to make a decision.
Reference List
Provost, F. and Fawcett, T. (2013) Data sciences for business: what you need to know about data mining and data-analytics thinking. Sebastopol, CA: OReilly Media.
Gupta, B. et al. (2017) Analysis of various decision tree algorithms for classification in data mining, International Journal of Computer Applications, 163(8), pp. 1519.
Jaworski, M., Duda, P. and Rutkowski, L. (2018) New splitting criteria for decision trees in stationary data streams, IEEE Transactions on Neural Networks and Learning Systems, 29(6), pp. 25162529.
Kumar, V. and L., M. (2018) Predictive analytics: a review of trends and techniques, International Journal of Computer Applications, 182, pp. 3137.
Patel, H. and Prajapati, P. (2018) Study and analysis of decision tree based classification algorithms, International Journal of Computer Sciences and Engineering, 6, pp. 7478.
Singer, G., Anuar, R. and Ben-Gal, I. (2020) A weighted information-gain measure for ordinal classification trees, Expert Systems with Applications, 152.
Singer, G. and Cohen, I. (2020) An objective-based entropy approach for interpretable decision tree models in support of human resource management: the case of absenteeism at work, Entropy, 22(8).
Thomas, T., P. Vijayaraghavan, A. and Emmanuel, S. (2020) Applications of decision trees, in Machine Learning Approaches in Cyber Security Analytics. Singapore: Springer, pp. 157184.
Yeo, B. and Grant, D. (2018) Predicting service industry performance using decision tree analysis, International Journal of Information Management, 38(1), pp. 288300.
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