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Evaluating model performance requires various metrics from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
When you create a machine learning model, you need to know how well it performs in order to make improvements. Evaluating the performance of a model requires looking at various metrics. These metrics help you understand how accurate your model is and where it can be improved. One common metric for evaluating classification models is accuracy. Accuracy tells you the percentage of correctly classified instances out of the total instances. While accuracy is a good starting point, it may not tell the whole story. For example, if you have imbalanced classes, accuracy may not be a reliable metric. Precision and recall are two other important metrics for evaluating classification models. Precision tells you the percentage of correctly classified positive instances out of all instances classified as positive. Recall, on the other hand, tells you the percentage of correctly classified positive instances out of all actual positive instances. These two metrics can give you a better understanding of how well your model is performing, especially when dealing with imbalanced classes. Another important metric for evaluating classification models is the F1 score, which is the harmonic mean of precision and recall. The F1 score gives you a balanced view of both precision and recall and can be a good overall metric for model performance. For regression models, one common metric is the mean squared error (MSE). MSE measures the average squared difference between the predicted values and the actual values. A lower MSE indicates a better-performing model. In addition to these metrics, there are many others you can use to evaluate model performance, depending on the specific problem you are trying to solve. By looking at a combination of metrics, you can get a more comprehensive understanding of how well your model is performing and where it can be improved.
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