SVM(Support Vector Machines)
One Sentence Description
SVM is a machine learning algorithm that finds the optimal boundary to separate data points into different classes, maximizing the margin between them, and can handle both linear and nonlinear data.
What is it
in which field
Machine Learning –> Supervised Learning
SVM is memory-efficient, robust, versatile, and effective in high-dimensional spaces. It also uses the kernel trick to solve complex classification problems that other algorithms cannot handle.
SVM (Support Vector Machine) works by finding the hyperplane that best separates the data points into different classes using a kernel function. It selects the hyperplane that maximizes the margin between the classes and identifies the closest data points to the hyperplane, called support vectors. It then uses these support vectors to classify new data points.
SVM stands for Support Vector Machine, which is a type of supervised learning algorithm used for classification and regression analysis. It is widely used in machine learning and data mining for pattern recognition and data classification. The main objective of SVM is to identify the hyperplane that maximally separates the two classes in a given dataset. This algorithm works by mapping the input data into a high-dimensional feature space, where the data can be separated by a hyperplane. SVM is a powerful machine learning algorithm that can handle complex datasets with high accuracy.
import os print(os.name) abc =1 for i in range(1,5): print(i)
Brain Mapping(Human Visible)