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Evaluation metrics are used to assess model performance from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Evaluation metrics play a crucial role in the data science process by providing a way to measure how well a model is performing. These metrics are essential in determining the effectiveness of a model in solving the specific problem it was designed for. By evaluating the performance of a model using these metrics, data scientists can gain insights into its strengths and weaknesses, which can then be used to make improvements. There are various evaluation metrics that can be used to assess model performance, depending on the nature of the problem being solved. For example, in a classification problem, metrics such as accuracy, precision, recall, and F1 score can be used to evaluate how well the model is classifying the data. On the other hand, in a regression problem, metrics such as mean squared error, mean absolute error, and R-squared can be used to assess the model's ability to predict continuous values. It is impo...
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