Fully Explained SVM Classification with Python

How the classification problem is solved with a real-life example.

In this article, we will discuss the most used machine learning algorithm in classification problems. The support vector machine (SVM) algorithm is used for regression, classification, and also for outlier detection.The hyper line or hyperplane are separated by the decision points or support vectors. The support vectors are the sample points that provide maximum margin between the closest different class points. This separation plane is called margin. The error will be less with a larger margin and the rate of miss-classification is also less.

The margin of SVM. A photo by Author

The above photo shows the linear hyperplane to divide the different classes. But, we can choose different criteria to divide the classes by choosing the different kernel function parameters given in the classification classes in SVM for the decision points. The different kernels are linear, rbf, polynomial, and sigmoid(tanh).

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Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.