The results of the study revealed that diabetes prediction models showed creditable performance rates using decision tree classifier. Even though, CART, C4.5, and ID3 are popular techniques, MARS and CHAID are less investigated. On the other hand, as accuracy is widespread, the significance of ...
Various decision tree models were used in this research. Methods: The records of 583 patients in the North East of Andhra Pradesh, India, registered at the University of California in 2012, were collected. Decision tree models were compared by three measures of sensitivity, accur...
Scikit learn is providing easy access to the classification algorithm by using different classifiers. The decision classifier function is breaking down the dataset into smaller subsets by using different criteria. This sorting criterion is used to divide the dataset by using a number of examples with ...
Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan,...
Decision tree (DT) and ensemble tree (ET) According to Khan et al.79, the machine learning (ML) algorithm is an essential component of the intelligent algorithm. Ensemble modeling is a process in which numerous models are developed to predict an outcome, either by employing various modeling te...
Second, we also examine the effectiveness of classifying two patterns using the Decision Tree algorithm. The final number of states (y1) can be regarded as results with dozens of dimensions for the number of states (y1), which may range from 1 to 32. We adopt to reduce the dimensions of...
(2017) . The LightGBM can find the best split point in learning a decision tree the fastest due to its histogram-based algorithm and leaf-wise splitting strategy. The main cost in traditional GBDT methods lies in learning decision trees, and the most time-consuming part of learning a ...
had compared balancing approaches of under-sampling, oversampling, SMOTE, and ADASYN with SVM, ANN, C5.0 tree, and CHAID tree to predict in-hospital mortality from hospital-acquired infections in trauma patients. They reported that among these ML algorithms, the SVM algorithm by SMOTE balancing ...
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DeclarationI,HaroldBukoDADYEherebydeclarethattheworkpresentedinthisMaster’sthesistitled“EffectsofDifferentPre-processingStrategies:AComparativeStudyonDecisionTreeAlgorithms”ismyoriginalworkandhasnotpresentedelsewhereforanyacademicqualification.Wherereferenceshavebeenusedfrombooks,publishedpapers,reportsandwebsites,itis...