What is Interpolation?
Interpolation is making an educated guess with the information within a certain data set. It is a “best guess” using the information you have at hand.
A Practical Example
At 10 am it was 75 degrees, at 11 am it was 80 degrees, at noon it was 83 degrees. What temperature was it at 10:30 am? While there is no way to know exactly, we know what is unlikely. For example, it probably was not below freezing, or suddenly over a hundred. We can use the data points we consider to be true and determine what a likely answer for a problem would be. At ten thirty, it was probably around 78, but we did not collect data at that time and can only interpolate.
Often, coming up with a function that fits those data points, so that you can come up with an interpolated value at any point in the function domain, is a quicker and more accurate way to come up with data.
Applications in Artificial Intelligence
Interpolation is not often something that’s useful in machine learning, but rather something that we often use AI for. Feeding data to a computer and allowing it to make educated guesses for us, especially when it comes to millions of lines of data, is helpful in many different fields.
In theory, interpolation is also useful in extricating data about situations, and using known experiences to expand knowledge into areas that are unknown. This is usually referred to as extrapolation, however.
