We will now wrap up this chapter with spline interpolation. In this section, we will look at interpolation using a single variable and interpolation using multiple variables. We will look at a toy example using the univariate case but also demonstrate the actual use of interpolation in image resizing.
I should probably start by saying that, even though I've included this section in this chapter about regression, interpolation and regression are not the same. With regression, we infer a function's values in the presence of noise, as shown in the following diagram:
![](https://static.packt-cdn.com/products/9781838823733/graphics/assets/7152a418-031d-431d-a374-ab807d0db271.png)
With interpolation, we have the exact values of a function at some data points, and we seek to estimate the values of the function at other locations, as shown in the following diagram:
![](https://static.packt-cdn.com/products/9781838823733/graphics/assets/44c11a1f-f987-4d1e-9815-84a6fdf57c4e.png)
Therefore, there can be no noise with interpolation. Regression falls into the realm of statistics,...