Support vector machine classifier is one of the most popular machine learning classification algorithm. Svm classifier mostly used in addressing multi-classification problems. If you are not aware of the multi-classification problem below are examples of multi-classification problems.
Multi-Classification Problem Examples:
- Given fruit features like color, size, taste, weight, shape. Predicting the fruit type.
- By analyzing the skin, predicting the different skin disease.
- Given Google news articles, predicting the topic of the article. This could be sport, movie, tech news related article, etc.
In short: Multi-classification problem means having more that 2 target classes to predict.
In the first example of predicting the fruit type. The target class will have many fruits like apple, mango, orange, banana, etc. This is same with the other two examples in predicting. The problem of the new article, the target class having different topics like sport, movie, tech news ..etc
In this article, we were going to implement the svm classifier with different kernels. However, we have explained the key aspect of support vector machine algorithm as well we had implemented svm classifier in R programming language in our earlier posts.
Read the complete post: svm classifier implementation in python scikit-learn