# Understanding Precision, Recall, and F1-Score in Classification Metrics

Precision, recall, and F1-score are crucial metrics in the context of classification problems, particularly when classes are imbalanced or when certain types of errors are more costly than others. Here’s a brief overview of each:

### Precision:

Precision answers the question: “Of all the instances the model predicted as positive, how many were actually positive?”

It is the ratio of correctly predicted positive observations to the total predicted positives. Precision=(True Positives) / (True Positives+False Positives)

High precision means that false positive errors are low. In other words, when the model predicts positive, it’s likely correct.

### Recall (or Sensitivity or True Positive Rate):

Recall answers the question: “Of all the actual positives, how many did the model correctly predict as positive?”

It is the ratio of correctly predicted positive observations to the all actual positives in the data. Recall=(True Positives) / (True Positives+False Negatives)

High recall means that false negative errors are low. That is, most of the positive instances are captured by the model.

### F1-Score:

The F1-Score is the harmonic mean of precision and recall. It seeks to balance the two.

It’s particularly useful when the distribution of classes is uneven, and you want to seek a balance between precision and recall. F1-Score=2× [( Precision×Recall ) / (Precision+Recall) ]

F1-Score is a good metric to use when both false positives and false negatives are important to consider. It will only get a high value if both precision and recall are high.

### An Analogy:

Imagine you’re a detective trying to catch criminals.

Precision: Out of all the people you arrested (predicted as criminals), how many were actual criminals? If you arrested 10 people and only 5 were criminals, your precision is 0.5.

Recall: Out of all actual criminals, how many did you manage to arrest? If there were 20 criminals and you arrested only 5 of them, your recall is 0.25.

F1-Score: It balances precision and recall. If you’re arresting a lot of innocent people (low precision) or letting many criminals go free (low recall), your F1-score will be low.

Author: user