#One and all how to#

X = data # the feature matrix is labeled with 'X' inside the file mat file in Python using the loadmat function from the scipy.io module.

No big deal, since it’s pretty easy to import a. mat file is because this problem is originally a Matlab assignment. The training data is stored in a file called digits.mat. Here are some examples from our training sample of 5000 images: We’re going to treat each pixel as an individual feature, which adds up to 400 features per image. Here the number of classes $N$ is equal to 10, which is the number of different digits. The goal in this problem is to identify digits from 0 to 9 by looking at 20x20 pixel drawings. We’re going to use this one-vs-all approach to solve a multi-class classification problem from the machine learning course thought by Andrew Ng. So the only thing we have to do now really is to train $N$ binary classifiers instead of just one.

#One and all how to#

We already know from the previous post how to train a binary classifier using logistic regression. Then those $N$ classifiers are collectively used for multi-class classification as demonstrated below: One-vs-all classification is a method which involves training $N$ distinct binary classifiers, each designed for recognizing a particular class. In this case, we’ll have to train a multi-class classifier instead of a binary one. Say we have a classification problem and there are $N$ distinct classes. In this post, we're going to build upon that existing model and turn it into a multi-class classifier using an approach called one-vs-all classification.įirst of all, let me briefly explain the idea behind one-vs-all classification. Previously, we talked about how to build a binary classifier by implementing our own logistic regression model in Python.