What’s a Transductive Model?
A transductive model is a predictive analytics model that makes use of distances or similarities. Contrary to inference models that make use of induction and deduction to make their predictions, transductive models tend to be direct. Oftentimes, they don’t even have a training phase in the sense that the model “learns” as it performs its predictions on the testing set. Transductive models are generally under the machine learning umbrella and so far they have always been opaque (black boxes).
What’s Transparency in a Predictive Analytics Model?
Transparency is an umbrella term for anything that lends itself to a clear understanding of how it makes its predictions and/or how to interpret its results. Statistical models boast transparency since they are simple enough to understand and explain (but not simplistic). Transparency is valued greatly particularly when it comes to business decisions that use the outputs of a predictive model. For example, if you decide to let an employee go, you want to be able to explain why, be it to your manager, to your team, or the employee himself.
Transparent kNN sounds like an oxymoron, partly because the basic algorithm itself is a moron. It's very hard to think of a simpler and more basic algorithm in machine learning. This, however, hasn't stopped people from using it again and again due to the high speed it exhibits, particularly in smaller datasets. Still, kNN has been a black box so far, despite its many variants, some of which are ingenious indeed.
Lately, I've been experimenting with distances and on how they can be broken down into their fundamental components. As a result, I managed to develop a method for a distance metric that is transparent by design. By employing this same metric on the kNN model, and by applying some tweaks in various parts of it, the transparent version of kNN came about.
In particular, this transparent kNN model yields not only its predictions about the data at hand but also a confidence metric (akin to a probability score for each one of its predictions) and a weight matrix consisting of the weight each feature has in each one of its predictions. Naturally, as kNN is a model used in both classification and regression, all of the above are available in either one of its modalities. On top of that, the system can identify what modality to use based on the target variable of the training set.
For now, I’ll probably continue with other, more useful matters, such as feature fusion. After all, just like most machine learning models, kNN is at the mercy of the features it is given. If I were in academic research, I’d probably write a series of papers on this topic, but as I work solo on these endeavors, I need to prioritize. However, for anyone interested in learning more about this, I’m happy to reply to any queries through this blog. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.