In this step, we will check the accuracy report of the model. We will get the following values.
Accuracy: The accuracy is the most important and popular metric for model validation. The ratio of the correctly predicted observation to the total observation is called the accuracy. In general, a high accuracy model is not always preferable, as the accuracy metric only works well with symmetric datasets where values of false positives and false negatives are almost the same.
Now, we will have a look at the formula of accuracy:
Here, we have the following:
- TP is true positive
- TN is true negative
- FP is false positive
- TP is true positive
Precision: The ratio of correctly predicted positive observations (TP) to the total predicted positive observations (TP + FP) is called precision. This is the formula for precision:
Recall: The ratio of correctly predicted positive observations (TP) to all the observations in an actual class (TP + FN) is...
