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A Novel Thresholding for Prediction Analytics with Machine Learning Techniques


Shakir Khan, Reemiah Muneer Alotaibi


Vol. 23  No. 1  pp. 33-40


Machine-learning techniques are discovering effective performance on data analytics. Classification and regression are supported for prediction on different kinds of data. There are various breeds of classification techniques are using based on nature of data. Threshold determination is essential to making better model for unlabelled data. In this paper, threshold value applied as range, based on min-max normalization technique for creating labels and multiclass classification performed on rainfall data. Binary classification is applied on autism data and classification techniques applied on child abuse data. Performance of each technique analysed with the evaluation metrics.


Machine learning, binary classification, multiclass classification, multi-label classification, threshold, cluster-then-predict.