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Evaluation metrics help in assessing the performance of machine learning models from "summary" of Machine Learning by Stephen Marsland
Evaluation metrics play a crucial role in the world of machine learning, as they provide a way to quantitatively measure the performance of different models. These metrics allow us to assess how well a particular model is performing in terms of its ability to make accurate predictions on unseen data. Without evaluation metrics, it would be challenging to determine which model is the most effective for a given task. One common evaluation metric used in machine learning is accuracy, which measures the proportion of correct predictions made by a model. While accuracy is a straightforward metric to understand, it may not always be the most suitable for assessing the performance of a model, especially in situations where the dataset is imbalanced. In such cases, other metrics like precision, recall, and F1 score can provide a more comprehensive view of a model's performance. Precision, for example, measures the proportion of true positive predictions out of all positive predictions made by the model. It is particularly useful when the co...
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