Implementing different kernels of SVC Algorithm on the Iris Dataset

Using Python to implement the various SVC kernels on the Iris Dataset

Photo by Fanny Côté on Unsplash

In this article, we will go through the SVC algorithm in the Sklearn library and experiment with the different kernels on the Iris Dataset.

Support Vector Classifier

Support Vector Classifier (SVC) is a supervised machine learning model used for two-group classification problems. After giving an SVC model set of labeled training data for each category, they’re able to categorize new test data.

SVC Classifier (Image from Wikipedia)

SVM classifies data based on the plane that maximizes the margin. The SVM decision boundary is straight. SVM is a really good algorithm for image classification. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback. This is also true for image segmentation systems, including those using a modified version SVM that uses the privileged approach.

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