While there are many different types of classification algorithms, evaluation metrics more or less shares similar principles. In a supervised classification problem, there exists a true output and a model-generated predicted output for each data point. For this reason, the results for each data point can be assigned to one of four categories:
- True positive (TP): Label is positive and prediction is also positive.
- True negative (TN): Label is negative and prediction is also negative.
- False positive (FP): Label is negative but prediction is positive.
- False negative (FN): Label is positive but prediction is negative.
Now, to get a clearer idea about these parameters, refer to the following figure:
Figure 23: Prediction ...
