Hands-On Deep Learning with R

Machine learning metrics

Choosing the wrong metric will make it very difficult to evaluate performance and, as a result, improve our model. Therefore, it is very important to choose the right metric. Let's take a look at the following machine learning metrics:

• Accuracy: The simplest evaluation metric is accuracy. Accuracy measures the difference between the predicted value and the actual value. This metric is easy to interpret and communicate; however, as we mentioned earlier, it doesn't measure performance well when used to evaluate a highly unbalanced target variable, for example.
• Confusion Matrix: The confusion matrix provides a convenient way to display classification accuracy along with Type I and Type II errors. The combined view of ...