classifier

The goal is to create a computer classifier that succeeds and fails in the same cases humans do.

And of course, you can fire up a classifier in SciPy without knowing any of this at all.

Though average classifier precision is relatively high (0.80), one in five users identified as antisocial are nonetheless misclassified.

You don't want a classifier to say sofa, when it's a picture of a person on a sofa.

It's worth pointing out that a naive bayes or k-nearest-neighbor classifier can similarly generate examples of valid inputs.

As an extreme example, a k-nearest-neighbors classifier is a model that consists of nothing except verbatim examples from the training set, but that doesn't mean it's always overfit.

You should create a two-class classifier to differentiate the two sessions rather than just eyeballing it.

Google is having PR problems because their image classifier labeled black people as gorillas.

They can probably use their classifier in feedback mode to generate a canonical image of what the gorilla recognizer is looking for.

Enhance the accuracy of our machine learning classifier, which infers what types of work contractors specialize in based off their transactional histories.

How about detecting vector edge shapes and unifying that result with the existing classifier?

If you were writing an automated Bayesian classifier to find good engineers, males would get one measly additional point towards the refer-to-a-human threshold.

It's needed; "Why did the classifier do that " is starting to become a big problem.

A k-nearest neighbor classifier could be an example of a "simple" algorithm for intelligence, assuming of course, that you've collected a tremendous number of accurate (input, intelligent response) pairs!

I recently had the need for Bayes Classifier[1] in a couple of projects, so I wrote a service that exposes one through an API.