2017
DOI: 10.13005/ojcst/10.04.11
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Comparision Between Accuracy and MSE,RMSE by Using Proposed Method with Imputation Technique

Abstract: Presence of missing values in the dataset leads to difficult for data analysis in data mining task. In this research work, student dataset is taken contains marks of four different subjects in engineering college. Mean, Mode, Median Imputation were used to deal with challenges of incomplete data. By using MSE and RMSE on dataset using with proposed Method and imputation methods like Mean, Mode, and Median Imputation on the dataset and found out to be values of Mean Squared Error and Root Mean Squared Error for… Show more

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Cited by 18 publications
(11 citation statements)
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“…The magnitude of the RMSE value depends on the value of the dataset used; the greater the RMSE value, the better the accuracy of the model. However, the small value of RMSE also depends on how much value the dataset used [26]. The following formula was used to normalize the RMSE value:…”
Section: Resultsmentioning
confidence: 99%
“…The magnitude of the RMSE value depends on the value of the dataset used; the greater the RMSE value, the better the accuracy of the model. However, the small value of RMSE also depends on how much value the dataset used [26]. The following formula was used to normalize the RMSE value:…”
Section: Resultsmentioning
confidence: 99%
“…42 Node purity increase looks at the changes in node purity after splits on the variable, 43 while the MSE increase is based on the decrease in predictive accuracy of the forest after perturbation of the variable. 44 If all the four indicators show that a feature is important, then we recognize the importance of that feature. The feature importance analysis was conducted by R 4.1.2, with the help of the "randomForest" and "randomForestExplainer" packages.…”
Section: Development Of the Multispecies-toxicity Prediction Model Ba...mentioning
confidence: 99%
“…As a result of the analyzes performed on 10 different datasets, k-NN came to the fore as the most unsuccessful missing value imputation method. Classification accuracy rate, Mean Squared Error (MSE) [48], Root Mean Square Error (RMSE) [48], [49] and Mean Absolute Error (MAE) [49] are generally preferred as metrics for success criteria. In another performance analysis performed on 5 different datasets [50], it has been observed that the C5.0 decision tree method fills the missing values more successfully than the other two methods, the k-NN algorithm also gives good results, but the calculations take a lot of time in large datasets, and mean method can give good results only if the percentage of missing value is below 5%.…”
Section: Decision Tree (Dt)mentioning
confidence: 99%