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- W4285325185 abstract "Three nonlinear methods are evaluated in this paper to get more accuracy on Wisconsin Breast Cancer Data. The application of two data transformation techniques is explored in this project after removing two highly correlated predictors. Additionally, the different train-test split procedure is also assessed in this project. It is observed that Neural Net achieved more than 99% accuracy while using Train: 80%/Test: 20% on standardized data. At the visual comparison of three models, using the ROC curve, Neural Net performed the best." @default.
- W4285325185 created "2022-07-14" @default.
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- W4285325185 date "2021-12-01" @default.
- W4285325185 modified "2023-09-27" @default.
- W4285325185 title "Application of Data Transformation Techniques and Train-Test split" @default.
- W4285325185 cites W2990787218 @default.
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- W4285325185 doi "https://doi.org/10.1109/csci54926.2021.00079" @default.
- W4285325185 hasPublicationYear "2021" @default.
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