AI News, Understanding Support Vector Machine algorithm from examples (along with code)

Understanding Support Vector Machine algorithm from examples (along with code)

Note: This article was originally published on Oct 6th, 2015 and updated on Sept 13th, 2017 Mastering machine learning algorithms isn’t a myth at all.

Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

In this article, I shall guide you through the basics to advanced knowledge of a crucial machine learning algorithm, support vector machines.

However,  it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

In Python, scikit-learn is a widely used library for implementing machine learning algorithms, SVM is also available in scikit-learn library and follow the same structure (Import library, object creation, fitting model and prediction).

The creation of a support vector machine in R and Python follow similar approaches, let’s take a look now at the following code:

Let’s look at the example, where we’ve used linear kernel on two feature of iris data set to classify their class.

Example: Have linear kernel Example: Have rbf kernel Change the kernel type to rbf in below line and look at the impact.

would suggest you to go for linear kernel if you have large number of features (>1000) because it is more likely that the data is linearly separable in high dimensional space.

 I discussed its concept of working, process of implementation in python, the tricks to make the model efficient by tuning its parameters, Pros and Cons, and finally a problem to solve.

Support Vector Machine - Georgia Tech - Machine Learning

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Still Support Vector Machines - Georgia Tech - Machine Learning

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