Support Vector Machines on the Breast Cancer Wisconsin (Diagnostic) Data Set
Attribution: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurelien Geron (O'Reilly). Copyright 2019 Kiwisoft S.A.S, 978-1-492-03264-9
Machine Learning Practice. Implimenting the project following the Chapter-5 on O'REILLY's Hands-On Machine Learning.
Goal: Predict breast cancer given all other values with the use of SVMs.
Approach:
- Supervised Learning task, because given labeled traning examples.
- Classification task.
- There is no continuous flow of data, no need to adjust to changing data, and the data is small enough to fit in memmory: Batch Learning
Data: Breast Cancer Wisconsin (Diagnostic) Data Set | Kaggle
Project Author: Maksim Ekin Eren
SVM's goal is to have largest possible margin between decision boundary that separetes the classes and the training instances.