Is Decision Tree Obsolete in Its Original Form? A Burning Debate

Kaul, Sameer; Fayaz, Sheikh Amir; Zaman, Majid; Butt, Muheet Ahmed

doi:10.18280/ria.360112

Cited by 14 publications

(12 citation statements)

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“…In [13], an upgraded vision of execution results was presented. Given their high accuracy, optimized parameters improved tree pruning strategies (ID3, C4.5, CART, CHAID [14], and QUEST [15,16]) are commonly used by all recognized information classifiers. The partitioned datasets are used in preparing tests from a tremendous information set, which in turn influences the accuracy of the test set.…”

Section: Related Workmentioning

confidence: 99%

Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

Hafez¹,

Elfakharany²,

Abohany³

et al. 2023

Computers, Materials &Amp; Continua

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Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm is widely used to analyze big data. Selecting the optimal depth of DT is time-consuming process as it requires many iterations. In this paper, we have designed a modified version of a (DT). The tree aims to achieve optimal depth by self-tuning running parameters and improving the accuracy. The efficiency of the modified (DT) was verified using two datasets (airport and fire datasets). The airport dataset has 500000 instances and the fire dataset has 600000 instances. A comparison has been made between the modified (DT) and standard (DT) with results showing that the modified performs better. This comparison was conducted on multi-node on Apache Spark tool using Amazon web services. Resulting in accuracy with an increase of 6.85% for the first dataset and 8.85% for the airport dataset. In conclusion, the modified DT showed better accuracy in handling different-sized datasets compared to standard DT algorithm.

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Section: Related Workmentioning

confidence: 99%

Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

Hafez¹,

Elfakharany²,

Abohany³

et al. 2023

Computers, Materials &Amp; Continua

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“…Some of the above literature studies of M5 model trees used in the geographical sciences are offered, however no study of M5 model trees used for rainfall prediction is presented. As a result, we conducted an experimental investigation of the M5 mode tree on rainfall data to see how accurate and practicable the model is [19][20][21]. This research will assist us in determining why this algorithm is not commonly used, as well as evaluating the performance of M5 mode tree on different types of data such as agricultural, health, and academic data [22,23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The table (Table 1) below shows a description of the data collected and the multiple parameters utilized for rainfall forecast in all zones of Kashmir province [18][19][20][21]. The final instances of the dataset comprise roughly 5500 elements after the preprocessing of the data.…”

Section: Materials and Datasetmentioning

confidence: 99%

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Fayaz¹,

Zaman²,

Kaul³

et al. 2022

RIA

Self Cite

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When using machine learning to predict a class with a continuous numeric value, there are several issues. Only a few machine-learning approaches are capable of doing so, but it remains one of the most difficult jobs to do. In this paper, we show how to use the M5 Model Tree, an approach that can handle continuous numeric data. This method is a stepwise procedure that employs linear functions at the leaf nodes of any created decision tree inducer (such as CART). These M5 model trees provide basic practical formulas such as standard deviation (SD), standard deviation reduction (SDR), cost-complexity pruning (CCP), and so on, which may be simply applied to different benchmark data by another user. This study examines the M5 Model Tree algorithm's capabilities for analysing rainfall data in the Kashmir portion of India's Union Territory of Jammu & Kashmir. One of the best suited models was the M5 model tree, which was built using (70–30) percent training and test ratios, respectively, and predicted an RMSE of 2.593, an MAE of 1.68, and a correlation coefficient (R2) of 0.478. Furthermore, M5 model trees produce models with a minimal number of trails, requiring less computing effort and making them more practical to use.

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“…Multiple study papers and polls have also demonstrated that married students perform better in school than single pupils. The research articles also stated that the older a student is, the greater the chances of achieving a higher GPA [26][27][28][29].…”

Section: Student Performance: Analysis Of the Futurementioning

confidence: 99%

Big Data in Academia: A Proposed Framework for Improving Students Performance

Banday¹,

Zaman²,

Quadri³

et al. 2022

RIA

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The way people learn has radically changed as a result of information technology. As an informal method of learning, fragmented learning has become a popular way to learn new technology and expertise. Academic organizations generate a large amount of heterogeneous data, and academic leaders want to make the most of it by analyzing the large amount of data in order to make better decisions. The volume isn't the only issue; the organization's data structure (structured, semi structured, and unstructured) adds to the complexity of academic work and decision-making on a daily basis. As big data has become more prevalent in educational settings, new data-driven techniques to enhance informed decision-making and efforts to improve educational efficacy have emerged. Traditional data sources and approaches were previously too expensive to obtain with digital traces of student behaviour, which offer more scalable and finer-grained comprehension and support of learning processes. This study provides a fragmented learning solution for students in a data environment that can suggest subjects to them based on their geographical location, gender, and district of residence, among other factors. This suggested framework is expected to play a key role in directing the development of a society that values lifelong learning.

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Is Decision Tree Obsolete in Its Original Form? A Burning Debate

Cited by 14 publications

References 0 publications

Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Big Data in Academia: A Proposed Framework for Improving Students Performance

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