Support Vector Machines
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About this course
Support vector machines or support vector networks are models based on supervised learning models that are primarily used for classification and regression analysis. Support vector machine or SVM is a robust and highly efficient prediction method with a firm foundation in statistical methods It was developed at AT&T Bell Laboratories by Vladimir Vapnik and his fellow workers.
SVM works like any other machine learning algorithm. An SVM is fed a set of training examples with each marked as belonging to one of two classes; its training algorithm builds a model that assigns new examples to one class or the other, so in essence, it is a non-probabilistic binary linear classifier. Geometrically, an SVM maps training examples to points in space and then tries to maximize the width of the gap between the two categories. For prediction, the input value is mapped into that same space and predicted to belong to a class based on which side of the margin they fall. In simple terms, an SVM classifies the data into classes by creating a line or a hyperplane which separates the data into classes.
When compared to more advanced systems like deep neural networks, SVM holds two main advantages i.e., SVM has higher speed and has better performance even with a small sample set.
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Course Outline
Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts.
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Frequently Asked Questions
What is a support vector machine? Explain it with an example?
A support vector machine (SVM) is a class of supervised deep learning algorithms that performs supervised learning for classification or regression of a data set segregated into classes.SVM finds huge applications in computational biology. One famous use case of SVM is 'Protein Fold and Remote Homology Detection.' Here SVM is used to model and analyze different models of the protein sequences. Internally, it uses different methods to solve the deployed kernel functions that help to find the similarity between different protein sequences.
What are support vector machines good for?
SVM finds application in many fields including computer vision, NLP, content moderation, review and recommendation systems, computational biology, sales and analytics, biotech etc.
When should I use SVM?
SVM is primarily a classification algorithm but it can be used for regression and outliers’ detection as well. SVM can be used irrespective of the linearity of the problem i.e., it can be applied for both linear and nonlinear problems. For SVM to give a good result, the following conditions must hold:
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The dataset has to be linearly separable else the data needs to be transformed using various Kernels offered. Transformation is not part of SVM and it can blow out the number of features.
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SVM’s performance deteriorates with an increase in the number of dimensions as compared to other methodologies such as a random forest. SVM uses constrained optimization problems and that deteriorates with an increase in input feature count.
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SVM is not suitable for a dataset where the number of features is much larger than the number of observations.
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Categorical variables are not handled out of the box by the SVM algorithm.
What is a kernel from SVM's perspective?
A kernel is a method of projecting a two-dimensional plane into a higher-dimensional space, so as to make it curved in the higher dimensional space. In layman's language, a kernel is a function that maps data from the low dimensional space into a higher-dimensional space.
What is the ‘kernel trick’ employed in SVM?
Kernel trick is employed to transform non-linear data to linear form. A Kernel Trick is a mathematical way to project non linear data onto a higher dimension space so as to make it easier to classify the data by dividing it linearly in a plane. This is done by using the Lagrangian formula using Lagrangian multipliers.
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