scikit-learn –Test Predictions Using Various Models: Classify Data with a Linear SVM| packtpub.com

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Here, we will focus on linear SVM more closely. While SVMs do not have an easy probabilistic interpretation, they do have an easy visual-geometric one. The main idea behind linear SVMs is to separate two classes with the best possible plane.
• Split the dataset of the first two features of the first two classes
• Create an SVM model instance
• Measure the performance of the SVM on the test set:

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