Overview
Tony Mu, Sahil Naikwadi, Harsh Patel, and Anmol Das Positive-Unlabeled Learned Binary Classifier Our goal for this project is to use the methods and techniques mentioned in "Learning classifiers from only positive and unlabeled data" to learn a binary classifier using positive and unlabeled datasets. We will attempt this by using a python program with the scikit-learn library. We plan to use a dataset from The UCI Machine Learning Repository that consists of 2 labels. We want to pick a dataset with only two labels because we can manually create a PU dataset with it by mixing the positive and negative samples but also we will have a dataset to train traditional classifiers to show performance comparisons between a PU learned classifier and a traditional classifier. We wish to create a PU learned classifier that will have a reasonable performance compared to a traditional classifier. Our performance criteria will be based on precision and recall of the classifiers. We can confi