Hey Machine Learning Enthusiasts, Today in this post we are going to learn about Support Vector Machine (SVM) in details. Also you will know that how it’s algorithm is also the very important part in machine learning, data science and many places so for that you have to read this post till the end.
Support Vector Machine (SVM) is one of the important algorithm of the supervised machine learning which are going to used in classification or the regression in both type of tasks. It is also known as it can manage the high dimensional data as it’s power to make a decision boundaries with the max number of margins. So now we will go into the deep like the concepts of SVM, Working principles, it’s applications and many more things relted to the support vector machine.
Table of Contents
What is Support Vector Machine (SVM)?
Support Vector Machine (SVM) is one the algorithm of the supervised machine learning that works like if you want to seprates the data points of various classes in feature spaces then it will finds the best suitable hyperplane that best seprates. The target is to get an optimal hyperplane that will maximises the margins. These data points which is too nearest is called as the support vectors and they will play a vital role in terms of defining the hyperplane.
SVM uses in classification and regression both but mostly for the binary classification as well as classification related tasks. However the most common used for binary classification due to the simple process and it’s effectivity.
How does Support Vector Machine (SVM) Works?
Mostly the svm works where the linear seprator can be useful or it can applied because it is transforming the data to hige dimensional spaces, so let’s we understand that how svm works.
Linearly Separable Data
If the data is linerly seprable then svm finds the hyperline which is seprating the classes with the maximum margins. The distance between the hyperline and the nearest data points from class, the large or the big margin then that much capability of that model we can say.
Non-Linear Data
If we talk about the situation when the data is not linear or not linearly seprable then SVM uses the trick called kernel to transform the data to high dimension space, where the hyperplane can seprate that related class. The function of kernel tells svm to operate in this feature space without the specific computing the co-ordinates of the data in that related space.
Support Vectors
Support vectors are nothing but the data points that is nearest to the hyperplane. They are totally particular to define the hyperplane because these points if removed then the position might can change.
Optimisation
Support vector machine can optimise the placement of the hyperplane by various optimisation problems like convex and many more. The target is to maximize the margins during the reducing the classification errors.
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Kernel Trick in Support Vector Machine
Kernel trick is nothing but the one of the technique that can classify that data is linearly not seprable in the feature space. Using this technique svm can trace the input data to high dimensional space where the sperator can be founf or we can say the common kernals can includes:
- Linear Kernel: It is only used when the data is linearly seprable.
- Sigmoid Kernel: It is almost same as neural network’s activation function, it is used in transformations which is not linear.
- Polynomial Kernel: It is plots the input data to a polynomial feature space.
- Radial Bias Function Kernel: It is plots the data to an infinite space and mostly it is used for the non linear data.
Kernel function depends on the data and the issue which is going on but the RBF is the most suited kernel due to it’s flexibleness.
Advantages and Disadvantages of Support Vector Machine
Advantages of SVM:
- Capable in High Dimensions: SVM doing the great in the high dimensional spaces where it is very important when the number of the dimensions get reachs the max number of the samples.
- Overfitting: SVM is very low when it get to overfitting, specially in the high dimensions spaces because it’s depends on the margins rather then the classification.
- Versatile: SVM can be used to non linear classification tasks with the kernel techniques.
Disadvantages of SVM:
- Computationally Fearful: it is very expensive because of the large datasets while the training the SVM.
- Choice of kernel: Performance of the SVM depends on the choices of the kernels and thier related functions which can be very difficult to decide.
- Not an Ideal for the Big Datasets: SVM is not suited as it ideal much because training time increases with thier size of the data so it is not ideal for big datasets.
Applications of Support Vector Machine
Support Vector Machine (SVM) has the lot of applications it different fields so let we discuss some of them to get know more about it:
Text Classification
Support vector machine is widely uses in the text classification tasks such as the spam detection, sentiment related analysis and many more. It can handle or manage the high dimensional data which makes it to an ideal for processing the text type of data.
Image Classification
Support vector machine is used in image classification related tasks such as object recognistion system, recognising the patten in images and many more. It can handle the high dimensional feature space which is particular good in this domain.
Writing Detection
SVM is very useful in writing recognition systems where it can helps in general between various writing characters, digits or many more.
Bioinformatics
SVM is very useful in bioinformatics like for classfies the proteins and other biological information. It’s can manage the data which make it a important tool in this field of bioinformatics.
Conclusion
Support Vector Machine (SVM) is a very powerful and useful algorithm of machine learning that better in high dimensional spaces and works very good for both non linear and linear classification related tasks. It has ability to make the decision boundaries with maximum margin which may the attractive choice for many of the applications from the text classification to an image recognition. However the computational complexity and if we talk about the challenge of the choosing the right kernel function are the factors to be consider when selecting thr SVM for your machine learning related tasks.
SVM have decent number of an advantages and limitations to applying for real world issues, whether you are works on small project or large scale project, SVM can be important in your machine learning toolkit!
Frequently Asked Questions
What is Support Vector Machine (SVM)?
Support Vector Machine is nothing but the algorithm of supervised machine learning which is mainly used for the classification related tasks.
How does SVM Handle Non-Linear Data?
SVM can handle the non linear data using kernel trick, which transforms the data to high dimensional space where it would find the linear seprator.
What are Support Vectors?
Support vectors are the data points which are very nearest to the hyperplane and the define it’s position.
What is the Kernel Trick in SVM?
The kernel trick is a technique which is used in SVM to maps the non linearly seprable data into a high dimensional spaces where it becomes to linearly sperable.
