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Machine learning model comparison metrics

Nov 11, 2022 · According to the above confusion matrix, classification accuracy will be. Classification accuracy = (45 + 30)/ (45 + 30 + 5 + 25) = 0.71. Here we can see the accuracy of the model is 0.71 or 71%..

These metrics can help research oriented people compare and quantify the results in order to write a good research paper. All the research papers include a result section where. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Hence, for a more robust comparison of multiple machine learning models, we can use AIC, AICc, BIC along with R, RMSE, and bias (Singh et al., 2021). Figure 2: Image from.

Machine learning is a major application of AI it is a phenomena by which system automatically learn and improve from real world experience in form of data on which it is trained or by observing the surroundings. Machine learning is changing the planet by transforming all segments including healthcare services, education, transport, food, entertainment, and.

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In simple words, AUC-ROC metric will tell us about the capability of model in distinguishing the classes. Higher the AUC, better the model. Mathematically, it can be created by plotting TPR (True Positive Rate) i.e. Sensitivity or recall vs FPR (False Positive Rate) i.e. 1-Specificity, at various threshold values.

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