Analytical Comparison Between the Information Gain and Gini Index using Historical Geographical Data

Zaman, Majid; Kaul, Sameer; Ahmed, M.N.

doi:10.14569/ijacsa.2020.0110557

Cited by 15 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment was conducted on a 70-30 ratio, with 70% serving as the training set and 30% serving as the testing set. The dataset contains four independent continuous parameters: minimum and maximum temperatures, humidity at two different intervals, and a continuous dependent variable, rainfall [24,25].…”

Section: Experimental Evaluationmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Experimental Evaluationmentioning

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

View full text Add to dashboard Cite

show abstract

“…Since, based on the same set of data various traditional and ensemble algorithms were implemented before and the accurate measure of each algorithm was calculated [1].…”

Section: 2comparati Ve Analysismentioning

confidence: 99%

“…These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…In decision tree learning, a machine learning algorithm called as Iterative Dicotomiser 3 (ID3) is used to generate decisions from the set of data. This construction of decisions uses recursive divide and conquer method (1) in which the training datasets are recursively partitioned into smaller subsets until no further partitions are required [10,11].…”

mentioning

confidence: 99%

See 2 more Smart Citations

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

Self Cite

View full text Add to dashboard Cite

Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

show abstract

Machine Learning: An Introduction to Reinforcement Learning

Fayaz¹,

Sidiq²,

Zaman³

et al. 2022

Machine Learning and Data Science

View full text Add to dashboard Cite

Analytical Comparison Between the Information Gain and Gini Index using Historical Geographical Data

Cited by 15 publications

References 0 publications

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

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

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Machine Learning: An Introduction to Reinforcement Learning

Contact Info

Product

Resources

About