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Evaluating model performance is essential for validation from "summary" of R for Data Science by Hadley Wickham,Garrett Grolemund
To determine how well a model is performing, it is crucial to evaluate its performance. This evaluation process is essential for validation because it allows us to understand how accurate and reliable the model is in making predictions. By assessing the model's performance, we can identify any potential issues or shortcomings that need to be addressed. There are various metrics and techniques available for evaluating model performance, such as accuracy, precision, recall, F1 score, ROC curves, and confusion matrices. These metrics provide valuable insights into different aspects of the model's performance, helping us to make informed de...
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