Hands-On Guide To Spline Regression

Hands-On Guide To Spline Regression

In this article, we will discuss spline regression with its implementation in python. Linear regression is one of the first algorithms taught to beginners in the field of machine learning.

Linear regression is one of the first algorithms taught to beginners in the field of machine learning. Linear regression helps us understand how machine learning works at the basic level by establishing a relationship between a dependent variable and an independent variable and fitting a straight line through the data points. But, in real-world data science, linear relationships between data points is a rarity and linear regression is not a practical algorithm to use.

To overcome this, polynomial regression was introduced. But the main drawback of this was as the complexity of the algorithm increased, the number of features also increased and it became difficult to handle them eventually leading to overfitting of the model. To further eliminate these drawbacks, spline regression was introduced.

In this article, we will discuss spline regression with its implementation in python.

What is Spline Regression?

Spline regression is a non-linear regression which is used to try and overcome the difficulties of linear and polynomial regression algorithms. In linear regression, the entire dataset is considered at once. But in spline regression, the dataset is divided into bins. Each bin of the data is then made to fit with separate models. The points where the data is divided are called knots. Since there are separate functions that fit the bins, each function is called piecewise step functions.

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Regression: Linear Regression

Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics.

A Deep Dive into Linear Regression

Lets begin our machine learning journey. A Deep Dive into Linear Regression. Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.