In the realm of machine learning (ML), interpolation is defined as the process of estimating the value of a function or within a dataset at points situated between known data points. It is frequently employed to fill in the blanks for missing values in a dataset or to eliminate noise and smooth out irregularities in the data.
In ML, there exist multiple distinct methods for interpolation, such as linear interpolation, polynomial interpolation, and spline interpolation. The selection of an interpolation method hinges upon the specific characteristics of the data and the objectives of the analysis.
Linear interpolation, a relatively uncomplicated process, involves fitting a straight line between two known data points and leveraging this line to compute the function's value at intermediate points. While it is fast and easy to implement, this method may not be ideal for data with more complex patterns.
During polynomial interpolation, fitting a polynomial function to the data points offers greater flexibility and is more suitable for data with intricate patterns. Spline interpolation, on the other hand, is used when the data exhibits smooth, continuous trends. It entails fitting a smooth curve to the data points.
In ML applications, interpolation can be utilized to handle missing values in a dataset. This proves particularly useful when dealing with incomplete or noisy data. Additionally, it can be applied to smooth out data irregularities, thereby enhancing the accuracy and robustness of machine learning models.