precision-and-recall

Vocabulary Word

Definition

'Precision and recall' are ways to measure a system's accuracy. 'Precision' means that when the system tries to find true things, it doesn’t say false things. 'Recall' means it can find as many true things as possible.

Examples in Different Contexts

In machine learning, 'precision and recall' are metrics used to evaluate the accuracy of a classification model. A data scientist might explain, 'Precision measures the proportion of true positive results in all positive predictions, while recall measures the proportion of true positive results in all actual positives.'

Practice Scenarios

Operations

Scenario:

The manufacturing process has been optimized, but we must continue refining it to minimize product defects.

Response:

Incorporating additional quality checks could improve the precision and recall of our manufacturing process.

Scenario:

Our machine learning model is consistently giving accurate predictions. But there is room for improvement to reduce instances of false positives.

Response:

We can boost the precision and recall of our model by implementing more thorough validation methods.