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Evaluation metrics provide insights into model performance from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Evaluation metrics are crucial in assessing the performance of machine learning models. These metrics provide valuable insights into how well a model is performing on a given task. By examining these metrics, we can understand the strengths and weaknesses of a model and make informed decisions on how to improve it. One common evaluation metric is accuracy, which measures the proportion of correctly classified instances out of all instances. While accuracy is a useful metric, it may not always provide a complete picture of a model's performance. For example, in imbalanced datasets where one class is much more prevalent than the others, accuracy can be misleading. Precision and recall are two other important metrics that provide more nuanced insights into model performance. Precision measures the proportion of true positive predictions out of all positive predictions, while recall measures the proportion of true positive predictions out of all actual positive instances. T...
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