MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
Here we discuss ‘threshold classifiers,’ a part of some machine learning systems that is critical to issues of discrimination. A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair, and how you might turn an unfair classifier into a fairer one. As an illustrative example, we focus on loan granting scenarios where a bank may grant or deny a loan based on a single, automatically computed number such as a credit score.
I’ve been trying to think of a way to describe how big Machine Learning is, and I think I finally have a decent one: Machine Learning is the new Statistics.
Every now and then I read a paper that makes a really strong connection with me, one where I can’t stop thinking about the implications and I can’t wait to share it with all of you. For me, this is one such paper. In the great see-saw of popularity for artificial intelligence techniques, symbolic reasoning and neural networks have taken turns, each having their dominant decade(s). The popular wisdom is that data-driven learning techniques (machine learning) won. Symbolic reasoning systems were just too hard and fragile to be successful at scale. But what if we’re throwing the baby out with the bath water? What if instead of having to choose between the two approaches, we could combine them: a system that can learn representations, and then perform higher-order reasoning about those representations? Such combinations could potentially bring to bear the fullness of AI research over the last decades.
Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively.