Support Vector Machine in scikit-learn

Support Vector Machine in scikit-learn- part 2

Posted Leave a commentPosted in Machine Learning, scikit-learn

continued from part 1 In [8]: print_faces(faces.images, faces.target, 400) Training a Support Vector Machine Support Vector Classifier (SVC) will be used for classification The SVC implementation has different important parameters; probably the most relevant is kernel, which defines the kernel function to be used in our classifier In [10]: from sklearn.svm import SVC svc_1 = SVC(kernel=’linear’) print […]

Support Vector Machine in scikit-learn

Support Vector Machine in scikit-learn – part 1

Posted Leave a commentPosted in Machine Learning, scikit-learn

Support Vector Machines has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the greatest advantages of Support Vector Machines is that they are very effective when working on high-dimensional spaces, that is, on problems which have a lot of features to learn from. […]