Xu, and T. Y. Chen. Testing and validating machine learning classifiers by metamorphic testing. Journal of Systems and Software, 84:544-558, Apr. 2011.Xiaoyuan Xie, Joshua WK Ho, Christian Murphy, Gail Kaiser, Baowen Xu, and Tsong Yueh Chen. Testing and validating machine learning ...
cross_validation import train_test_split x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=1/4,random_state=0) #Here we again split the training data further ##into training and validating sets. #observe that the size of the validating set is #1/4 of the training set an...
Machine learning testing is a critical process that involves evaluating and validating the performance of machine learning models to ensure their fairness, accuracy, and robustness. It is not all about model accuracy and performance. We have to look at model biases, false positives, false negatives...
Xie, X., Ho, J.W.K., Murphy, C., Kaiser, G., Xu, B., Chen, T.Y.: Testing and validating machine learning classifiers by metamorphic testing. J. Syst. Softw. 84(4), 544–558 (2011) Article Google Scholar Yan, B., Yecies, B., Zhou, Z.Q.: Metamorphic relations for data...
https://doi.org/10.1109/ASE.2019.00127 Xie X, Ho JWK, Murphy C, Kaiser G, Xu B, Chen TY (2011) Testing and validating machine learning clas- sifiers by metamorphic testing. J Syst Softw 84(4):544–558. https://doi.org/10.1016/j.jss.2010.11.920, http://www.sciencedirect.com/...
Simulation experiments were conducted in MATLAB using the Statistics and Machine Learning Toolbox for machine learning tasks. Feature calculations were performed using Numpy and Scipy, Natick, MA, USA. 4.1. Construction of the Dataset The dataset used for training and validating the proposed anomaly ...
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