Visualising True Positives and False Positives against Features with scikit-learn
Here I’m starting to look into the errors caused in the social media brand disambiguator project. Below I look at true and false positives (correct and mistaken is-a-brand classifications) and plot them against the number of features that two different classifiers can use to calculate their class membership probabilities. First I’m using the default LogisticRegression […]