How to use SVM Classifier and Regressor in Python
Support Vector Machine (SVM) can be used for both Classification and Regression tasks. In classification, the goal is to assign a label to a given input, like determining which digit the image represents (0-9) from an image of a handwritten digit. In Regression, the goal is to predict a continuous value instead of a discrete label.
SVMs work by finding the “Hyperplane” that separates the different classes in your data. A hyperplane is a decision boundary that separates the data into different classes. The idea is to find the hyperplane that gives the maximum margin, which is the distance between the hyperplane and the closest data points from each class, this is called “Maximum Margin Classification”
SVM algorithm uses a technique called “kernel trick”, which enables us to solve problems that are not linearly separable in their native space, by projecting them into a higher dimensional space where they become linear separable.
Scikit-learn is a popular python library for machine learning, it has a built-in implementation of SVM classification and Regression. The library provides several options for kernel functions like Linear, Polynomial and Radial Basis Function (RBF) to choose from.
You can also customize other parameters like the type of decision function and the penalization parameter, which can be set to adjust the trade-off between maximizing the margin and minimizing the misclassification rate.
In summary, SVM is a powerful algorithm that can be used for both classification and regression tasks, with the ability to handle non-linearly separable problems by applying kernel trick and with the help of library like scikit-learn it’s easy to implement and use.
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