What is Interpolation?

One thing that is rightly said about the term interpolation is that without interpolation all science would be impossible. And after such a strong statement it is very important to know what is interpolation and its basic working.

Learn Data Science from scratch through our Data Science Course

What is Interpolation?

Mathematical interpolation is a method for estimating a function’s value at locations when the function is implicitly specified. In order to predict the value of the function at additional places, a curve or surface that goes across a collection of known data points must first be found.

Numerical analysis, data engineering, computer graphics, and mathematics are just a few of the disciplines that frequently apply interpolation. It is widely used to analyze photos and videos, estimate values between known data points, and decrease the amount of data required to describe curves and surfaces.

There are several methods of interpolation, including linear interpolation, polynomial interpolation, and spline interpolation. The choice of interpolation method depends on the specific requirements of the application and the nature of the data being interpolated.

The following are some benefits of interpolation:

1. Value estimation: Smooth curves and surfaces can be produced using interpolation by using it to estimate values between known data points.
2. Data Compression: Interpolation can be utilized to minimize the quantity of data that must be kept or sent by decreasing the number of data points required to depict a curve or surface.
3. Image and Video Processing: Interpolation is a technique that is often used in picture and video processing to boost image resolution or smooth out pixelated images.
4. Numerical Analysis: Interpolation is frequently used in numerical analysis to resolve differential equations because it allows an equation’s solution to be approximated at intermediate places.

Interpolation is a helpful approach in general for estimating values between known data points, reducing the amount of data required to depict a curve or surface, or processing photos or videos.

To learn more check out Intellipaat’s Data Scientist training.

Interpolation Formula

The Interpolation formula is as follows-

y- y1= ((y2-y1)/ (x2- x1))* (x2- x1)

where

• Linear interpolation value is denoted by y.
• The independent variable is denoted by x.
• Values of the function at one point are denoted by x1,y1.
• Values of the function at another point are denoted by x2,y2.

Interpolation and Extrapolation

Interpolation and Extrapolation are both methods used to estimate the value of a function at a point based on known values at surrounding points. Here is a table showing the difference between Interpolation and Extrapolation.

Check out our blog on Data Science tutorial to learn more about it.

Types of Interpolation

Interpolation can be calculated in a variety of ways. A few methods of Interpolation are the following:

1. Linear Interpolation: A straightforward approach for predicting the value of a function at a position between two known data points by determining the equation of the line that connects these two points.
2. Polynomial Interpolation: A technique for predicting the value of a function at a position between two known data points by locating a polynomial that passes between the data points.
3. Spline Interpolation:  A method for predicting the value of a function at a position between two known data points by locating a smooth curve that passes between the data points and has continuous derivatives up to a particular order.
4. Cubic Interpolation: Determining a cubic polynomial that crosses through the data points and has constant first derivatives to estimate the value of a function at a position between two known data points.
5. B-spline Interpolation: A technique for predicting the value of a function at a position between two known data points by locating a spline that passes through the data points and is depicted as a linear combination of basis functions.
6. Multivariate Interpolation: A method for predicting the value of a function at a location in several dimensions between two known data points.
7. Radial Basis Function (RBF) Interpolation: A method of estimating the value of a function at a point between two known data points by finding a radial basis function that passes through the data points and is represented as a linear combination of basis functions.

The method of interpolation used is determined by the application’s unique needs and the type of data being interpolated. Simpler approaches, such as linear interpolation, are beneficial for basic estimations. And complicated methods, such as spline or polynomial interpolation, are useful for more accurate guesses of more complex functions.

Explaining Each Type in Detail

It is important to know about the above-discussed types or methods of interpolation and discuss a few in detail.

Polynomial Interpolation

Polynomial Interpolation is a method for estimating the value of a function at a point between two known data points by finding a polynomial that passes through the data points. In this method, the data points are represented as the coefficients of a polynomial equation, which is then used to estimate the value of the function at an unknown point.

Here’s the general form of the polynomial equation:

P(x) = a0 + a1x + a2x^2 + … + anx^n, where x is the independent variable, ai are the coefficients of the polynomial, and n is the degree of the polynomial, which is equal to the number of data points minus one.

The degree of the polynomial is determined by the number of data points, and the higher the degree, the more accurate the estimation. However, high-degree polynomials may also result in overfitting the data, which means that the polynomial may not generalize well to new data points. The choice of the degree of the polynomial depends on the specific requirements of the application and the nature of the data being interpolated.

Spline Interpolation

Spline Interpolation is a type of interpolation that uses piecewise polynomial functions, called splines, to estimate the value of a function between two known data points. It is a flexible method that allows for smooth and continuous interpolation between the data points.

The spline function is defined as a continuous and smooth polynomial function that passes through the data points and also satisfies certain conditions, such as continuity of the first and second derivatives. The most common type of spline is a cubic spline, which uses a third-degree polynomial for each piece.

The spline function is defined as follows:

S(x) = a0 + a1x + a2x^2 + a3x^3, where x is the independent variable, ai are the coefficients of the polynomial, and n is the degree of the polynomial.

Cubic Interpolation

Cubic Interpolation is a method of estimating the value of a function at a point between two known data points by fitting a cubic polynomial to the data. The cubic polynomial is chosen such that it passes through the two known data points and also has the same first and second derivatives at the endpoints. This results in a continuous and smooth estimate of the function between the data points.

The cubic polynomial function is defined as:

P(x) = a0 + a1x + a2x^2 + a3x^3, where x is the independent variable, ai are the coefficients of the polynomial, and n is the degree of the polynomial.

Multivariate Interpolation

Multivariate Interpolation is a method of estimating the value of a multivariate function at a point within the domain of the function based on known values at surrounding points. The goal is to approximate the unknown value of the function at the given point based on the known values at surrounding points.

There are various methods of multivariate interpolation, including polynomial interpolation, spline interpolation, and kriging. These methods differ in the mathematical models used to estimate the unknown values and the computational methods used to solve the problem.

Go through these Data Science Interview Questions and Answers to excel in your interview.

Summing Up

With the increasing amount of data being generated, interpolation techniques will continue to be important for analyzing and processing data efficiently. Furthermore, with advancements in technology, interpolation techniques are likely to become more sophisticated and accurate, allowing for better predictions and estimates.

In conclusion, interpolation is a valuable tool that has a range of applications and will likely continue to be used in the future.