Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits
Nirmla Sharma1, Sameera Iqbal Muhmmad Iqbal2
1Dr. Nirmla Sharma, Asst. Professor, Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia.
2Sameera Iqbal Muhmmad Iqbal, Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia.
Manuscript received on 26 May 2023 | Revised Manuscript received on 04 June 2023 | Manuscript Accepted on 15 June 2023 | Manuscript published on 30 June 2023 | PP: 77-81 | Volume-12 Issue-5, June 2023 | Retrieval Number: 100.1/ijeat.E41950612523 | DOI: 10.35940/ijeat.E4195.0612523
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Decision tree analysis is a predictive modelling tool used in various applications. It is constructed through an algorithmic technique that divides the dataset into different methods created under varied conditions. Decision trees are the most dominant algorithms that fall under the set of supervised algorithms. However, the Decision Trees’ appearance is modest and natural; there is nothing modest about how the algorithm drives the procedure by determining splits and how tree pruning happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter, into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. The Gini index is a level task that has been applied to assess the binary changes in the dataset, working with the definite object variable “Success” or “Failure”. Split creation essentially covers the dataset values. Decision trees employ a top-down, greedy method that has been recognised as recursive binary splitting. It provides statistics for 15 key facts about scholar statistics, including pass or fail rates on an online Machine Learning exam. Decision trees are a type of supervised machine learning. It has been commonly applied, with an informal implementation, and has been interpreted as deriving quantitative, qualitative, non-stop, and binary splits, providing consistent outcomes. The CART tree applies a regression technique to expected standards of non-stop variables. CART regression trees are a formal technique for understanding outcomes.
Keywords: Decision Trees, Gini index, Objective Parameter and Statistics.
Scope of the Article: Artificial Intelligence