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Support Vector Machine (SVM) Algorithm – Machine Learning | Everything You Need to Know

Last updated on 04th Nov 2022, Artciles, Blog

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    • In this article you will learn:
    • 1.What are the Support Vector Machines (SVM) in Machine Learning?
    • 2.Types of a Support Vector Machines Algorithm.
    • 3.Hyperplane and Support Vectors in a SVM Algorithm.
    • 4.How Does SVM Work in a Machine Learning?
    • 5.Applications of Support Vector Machine.
    • 6.Advantages and Disadvantages of a Support Vector Machine.
    • 7.Conclusion.

What are the Support Vector Machines (SVM) in Machine Learning?

The SVM model or Support Vector Machine model is the famous set of supervised learning models that are used for a regression as well as classification analysis. It is model based on a statistical learning framework and is known for being a robust and effective in multiple use cases. Based on non-probabilistic binary linear classifier support vector machine is used for the separating various classes with the help of various kernels. One of the major reasons companies are leaning towards a support vector machine models as compared to other models is because a Support Vector Machines have significantly higher accuracy that can be leveraged while using a decreased computation from system.

Types of a Support Vector Machines Algorithm:

Linear SVM: The Linear Support Vector Machine algorithm is used when have a linearly separable data. In a simple language if have a dataset that can be classified into the two groups using simple straight line call it linearly separable data and the classifier used for this is known as a Linear SVM Classifier.

Non-Linear SVM: The non-linear support vector machine algorithm is used when have a non-linearly separable data. In a simple language if have a dataset that cannot be classified into the two groups using a simple straight line and call it non-linear separable data and classifier used for this is known as Non-Linear SVM classifier.

Hyperplane and Support Vectors in SVM Algorithm:

Hyperplane:

When given set of points, there can be a multiple ways to separate a classes in an n-dimensional space. The way that SVM works, it can be transforms the lower dimensional data into the higher dimensional data and then separates out of points. There are multiple ways to separate a data, and these can be called a Decision Boundaries. However, main idea behind a SVM classification is to find best possible decision boundary. The hyperplane is an optimal, generalized and best-fit boundary for support vector machine classifier. For instance in two-dimensional space, as discussed in example, hyperplane will be a straight line. In a contrast if data exists in a three-dimensional space then hyperplane will exist in a two dimensions. A good rule of thumb is that for the n-dimensional space the hyperplane will be generally have n-1 dimension. The aim is to create the hyperplane that has highest possible margin to create generalized model. This indicates that there will be maximum distance between the data points.

Support Vectors:

The term support vector indicates that are have a supporting vectors to main hyperplane. If have the maximum distance between support vectors, it is indication of best fit. So, support vectors are vectors that pass through a closest points to hyperplane and affect an overall position of the hyperplane.

Support Vector Machine

How Do Find a Right Hyperplane?

Maximize a Margin Between Support Vectors:

The recommended way to find a right hyperplane is by maximizing a distance between a support vectors. Below,also visualize what this will look like in the two-dimensional space, this can also be done in the n-dimensional space, but it will be complex for us to visualize.

Transform a Lower Dimensional Data into the Higher Dimensional Data:

When transform a lower dimensional data into the higher dimensional data, with the help of a new features created, it separates a points in a higher dimension, and can then pass hyperplane with a more efficiency to segregate out data. This is done with help of a following steps:

  • Augment data with the some non-linear features that are computed using an existing features .
  • Find a separating hyperplane in higher dimensional space .
  • Project points back to original space.

How Does SVM Work in a Machine Learning?

SVM works based on a principle of maximizing a distance between support vectors. This ensures that have a maximum margin possible between points, thus, giving us generalized model. The aim of a Support Vector Machine classification is to the maximize a margin between the Support Vectors.

Linearly Separable Data:

Use a kernels in a support vector machines. SVM kernels are the functions based on which can transform a data so that it is simpler to fit a hyperplane to segregate a points better. Linearly separable points are consist of points that can be separated by simple straight line. The line has to have largest margin possible between closest points to form generalized SVM model.

Non-linear Data :

Non-linear data is data that cannot be separated by a simple straight line. And can separate out the classes by a mapping the data into a higher dimensional space such that are able to classify a points. Here, use a derived higher dimensional features from dataset itself. For instance, with the dataset that is present on the X and Y axis, will use a features such as X2, Y2, and XY to make higher dimensional model, project a data, make the hyperplane, and then revert a data to its original space.

The Kernel Trick:

The kernel trick is a “superpower” of Support Vector Machines. A Support Vector Machine uses a kernels, ls, which is a function based on which points can be segregated. The points that are no-linearly separable are projects to the higher dimensional space.

Applications of Support Vector Machine

Applications of a Support Vector Machine:

Email Classification: A support vector machine can be used for an email classification, where decide if an email is spam or ham .

Face Detection: Leveraging a SVM, can perform a face recognition, where train a model on dataset, and can predict. where a text in red indicates that an image has been incorrectly predicted. Can also get a metrics such as precision recall and f1-score for same.

Text Categorization: Categorization of the both inductive and transductive models is used for a training, and it uses various scores generated to compare with a threshold value.

Handwriting Recognition: SVM can also be used for the handwriting recognition, where are able to convert a hand-written text to machine-readable text.

Bioinformatics: This includes a cancer classification and also protein classification, where use a SVM to identify the classification of patients and genes based on a biological markers.

Advantages and Disadvantages of Support Vector Machine:

Advantages of SVM:

  • When there is clear margin of a separation between various classes, SVM works well.
  • Memory efficiency is one of the key advantages of a SVM, as it uses a subset of a training points as part of decision function support vectors.
  • SVM tends to be optimized algorithm when data exists in high dimensional space.
  • It works well when there is higher number of columns than a number of rows.
  • It is possible to use a different kernel functions to make a better models.

Disadvantages of SVM:

  • Need to be aware that when have a large number of the features, need to avoid the overfitting in a data and regularization of terms is critical.
  • Due to kernel trick SVM works by a projecting the data into higher dimension, so there is no probabilistic explanation to perform classification.
  • This can be done but it uses computationally intensive of five-fold cross-validation.

Conclusion:

The Support Vector Machine (SVM) is machine that is supervised to learn algorithms used for a both classification and regression. The SVM algorithm’s objective is to find hyperplane in an N-dimensional space that distinctly classifies a data points. In this blog, got to know about Kernel trick and understood different terminology associated with a Support Vector Machines.

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