Machine Learning, Numerical Methods, Cubicspline.
it name suggests, just approximates. When it comes to appropriateness, interpolation is appropriate to use in smoothing out such noisy data and not appropriate when data points are subject to experimental errors or other sources of significant error. Having large set of data points also may burden