TASK - 2(Class)

Support Vector Machine

Referring to 'Enhancing Malaysia Rainfall Prediction Using Classification Techniques'

A Support Vector Machine or in short, SVM could be a supervised machine learning model. It uses classification algorithms for classification problems containing two groups. When given sets of labeled training data to an SVM model, for each category, they can categorize new text. 

The algorithm distinctly classifies the data points by finding a hyperplane in an n-dimensional space(n is the number of aspects). 

Possible hyperplanes

Optimal hyperplane

There are many feasible hyperplanes that can be chosen to separate the two types of data points. The objective is to find a plane having maximum margin which is the maximum distance between the two data types. Maximizing this margin is necessary to provide some support so that future data points can be classified more precisely.

Hyperplanes are decision borders that classify the data points. The dimension of the hyperplane depends on the number of features of the space, For example if its a 2D feature, the hyperplane is a line(1D); for a 3D feature space, the hyperplane is a plane(2D) and so on.

Support vectors are the data points closest to the hyperplane and they determine the locale and inclination. They can be used to maximize the margin of the classifier.

Hyperplanes in 2D and 3D space.

All in all, a support vector machine lets you classify data that's linearly separable. For further studies, check out this video: