Apply Machine Learning on a Cancer Dataset. In this article, take a look at how to apply machine learning on a cancer dataset.
Support Vector Machines (SVM) are one of the most popular supervised learning methods in Machine Learning(ML). Many researchers have reported superior results compared with older ML techniques.
SVM can be applied on regression problems as well as classification problems, however, here I describe a classification application on a cancer dataset.
SVM has been widely used throughout ML, including medical research, face recognition, spam email, document classification, handwriting recognition. In the medical field, SVM has been applied by practitioners in:
Researchers have claimed better results than logistic regression and decision trees and also Neural Networks.
Overview of method
A popular classifier for linear applications because SVM’s have yielded excellent generalization performance on many statistical problems with minimal prior knowledge and also when the dimension of the input space(features) is very high.
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You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
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Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.
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