Evaluation metrics
Evaluating a model involves checking if the predicted value is equal to the actual value during the testing phase. There are various metrics available to check the model, and they depend on the state of the target variable.
For a binary classification problem, the predicted target variable and the actual target variable can be in any of the following four states:
Predicted | Actual |
Predicted = TRUE | Actual = TRUE |
Predicted = TRUE | Actual = FALSE |
Predicted = FALSE | Actual = TRUE |
Predicted = FALSE | Actual = FALSE |
When we have the predicted and actual values as same values, we are said to be accurate. If all predicted and actual values are same (either all TRUE or all FALSE), the model is 100 percent accurate. But, this is never the case.
Since neural networks are approximation models, there is always a bit of error possible. All the four states mentioned in the previous table are possible.
We define the following terminology and metrics for a model:
- True Positives (TP): All cases where the...
