Both are methods of measuring outcomes. The degree of accuracy quantifies how near the outcomes are to the natural or known value. Precision quantifies how similar the two outcomes are. Both are effective methods for tracking and reporting project outcomes.
In everyday life, the terms “accuracy” and “precision” are frequently interchanged. The key difference between precision and accuracy in Machine Learning lies in measurement. A measurement’s accuracy does not imply its precision and vice versa.
Accuracy vs Precision in Machine Learning
Precision and accuracy are two kinds of measurements that determine how near you are to striking a target or completing a goal. Machine Learning precision measures how near the calculated results are to one another, whereas accuracy deals with how close they are to the actual value of the measurement.
We can use the bullseye analogy to demonstrate their distinction. Consider playing darts. The objective (to strike the bullseye as often as possible) requires both exactness and accuracy. If you’re just accurate, you throw the darts to land as close to the bullseye, but you don’t always hit it. If you’re just exact, your darts will land close together but not necessarily near the bullseye. Only by combining both accuracy and precision will you achieve the best case scenario – always hitting bullseye.
What is accuracy?
When a measurement is accurate, it means that it closely matches the established standard for that quantity. If we predict the size of a project to be x and the completed size is equal to or extremely near to x, it is accurate but not exact. The nearer a system’s measurements is to the recognized value, the more accurate it is thought to be.
It is in human nature to make mistakes, but this can be. avoided with the aid of software.
What is precision?
An accurate measurement agrees with other measurements of the same object. As an example of project scoping, consider workload estimation. If we determine the size of multiple projects and find that they are all near to or identical to what we projected, we begin to grasp the precision of our predictions. But, above all, each project must be as precise as possible.