The model describes the class extremely well.

High Precision and High Recall

The model has a hard time describing the class but when it does describe the given class, it is good at doing so.

High Precision and Low Recall

The model describes the class well, but the data is unbalanced and the model picks up other classes.

Low Precision and High Recall

Model can not describe the class well. Handles the data poorly. Low precision and low recall is the least ideal for describing data.

Low Precision and Low Recall

There are four combinations of precision and recall:
(1) High Precision and High Recall
(2) High Precision and Low Recall
(3) Low Precision and High Recall
(4) Low Precision and Low Recall

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Recall, also known as the True Positive Rate (TPR), is defined as True Positives / (True Positives + False Negatives), i.e., what % of actual outcomes (labels) are correctly recognized.

Recall is important in tasks that required as many possible positives to be identified.

Recall is especially important in medicinal and legal applications (ex: fraud detection and tumor detection), and it is occasionally paired with someone who manually filters out all of the False Positives. Recall is also related to F1 Score and ROC Curves.


Recall of a Classification Model

Sensitivity is the probability of a correct prediction of positive classification; in other words, the probability of the true positive (TP).  

Sensitivity

In statistical analysis of a binary classification, the F1 score is a measure of a test's accuracy, defined as the average of precision and recall. It is the harmonic mean of the precision and recall, with the highest possible value being 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either the precision or the recall is zero.

Some applications for using F1 score include measuring search, document classification, and query classification performance. The F-score has been widely used in the natural language processing literature, such as in the evaluation of named entity recognition and word segmentation.

Some common criticisms of F1 score include that it gives equal importance to recall and precision. It also does not take into account true negatives, thus making it susceptible to unbalanced class bias.

$$F_1 = 2\frac{1}{(\frac{1}{precision}+\frac{1}{recall})}$$

F1 Score

Relation between Precision and Recall

F-Measure is a metric that incorporates aspects of both precision and recall, defined as:
$$F_\beta = \frac{(\beta^2 + 1)PR}{\beta^2P + R}$$
The $\beta$ parameter differentially weights the importance of recall $R$ and precision $P$, based perhaps on the needs of an application. Values of $\beta > 1$ favor recall, while values of $\beta < 1$ favor precision. When $\beta = 1$, the measure is simply the F-1 score, where precision and recall are equally balanced.

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