Support Vector Machine Classification in Scikit-learn
In this tutorial, you’ll learn about support vector machines, one of the most popular and widely used supervised machine learning algorithms.
Support Vector Machines are one of the most popular and widely used supervised machine learning algorithms. SVM offers very high accuracy compared to other classifiers such as logistic regression, and decision trees. SVM is known for its kernel trick to handle nonlinear input space. It is used in a variety of applications such as face detection, intrusion detection, classification of emails, news articles, and web pages, classification of genes, and handwriting recognition.
SVM is an exciting algorithm and the concepts are relatively simple. SVM classifier separates data points using a hyperplane with the largest amount of margin. That’s why an SVM classifier is also known as a discriminative classifier. SVM finds an optimal hyperplane which helps in classifying new data points.
For more such tutorials and courses visit DataCamp:
In this tutorial, you are going to cover the following topics:
- Support Vector Machines
- How does it work?
- Kernels
- Classifier building in Scikit-learn
- Tuning Hyperparameters
- Advantages and Disadvantages
- Conclusion
