Support Vector Machine in scikit-learn

Support Vector Machine in scikit-learn- part 2

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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 on 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. […]

Linear Classification Method

Linear Classification method with ScikitLearn

Posted on 1 CommentPosted in Machine Learning, scikit-learn

This blog is from the book and aimed to be as a learning material for myself only.Linear Classification method implements regularized linear models with stochastic gradient descent (SGD) learning. Each sample estimates the gradient of the loss at a time and the model updates along the way with a decreasing strength schedule (aka learning rate). SGD allows […]