pete_b_condon

You understand that the retail price of power is made up of many components, right?

Physical security, saftey, redundancy, and system strength are significant drivers of the network costs (typically about 40% of the bill to consumers).

Please save your snark for relevant conversations. The discussion here has nothing to do with your culture war on decarbonisation.

This is important background material into why I started publishing my ideas into Explainable AI. Very open to feedback, as always.

"I’m writing this in December of 2020" :)

That works to a point, but it doesn't necessarily find all the rules of the model. In the post I walked through a model with three training records (yellow, blue, red) which created six prediction boundaries. Half of the rules weren't covered by the training data, which makes them hard to find without an efficient algorithm to search out all possible rules. The risk of undiscovered rules is they may cause unexpected behaviour that leads to bad predictions - and if you haven't described the whole model then it will be impossible to know how many of these potentially bad predictions exist.

Yep, all the code is in there. I do have another piece of code that has all the algorithms in a single class (which makes it much easier to use), I'll double check that it's up to date and post that tonight.

No worries, I thought I was ok because the post links to the GitHub repo but I'll make the link more explicit in future.

Yep, I've added an email address now :)

Thanks :)

I've been trying to work out where to put updates, so far I've been using GitHub & Twitter (both @wagtaillabs). I'll keep posting to HN as well (I just had two orders of magnitude more traffic than any other day).

I'd be more than happy for any suggestions on places where people could follow (I've thought about an email list, but I'm not sure how many people actually read emails any more).

That's a very good question.

You're right that the way we typically train Tree Ensembles creates a massive number of rules, the walk through Random Forest has more than 100,000 leaves per Decision Tree. Once we start grafting it the number of rules starts to vastly outnumber the amount of training data.

I have some follow up articles planned that will cover this in more detail, but the short answer is that I feel that we often jump to overly complex models up front without fully considering whether the accuracy/complexity tradeoff it worth it. Using Amalgamate I showed how I could have the number of rules without significantly increasing validation error (+5%). I believe that if we're careful using model sophisticated techniques (i.e. Boosting and dense/fully connected/tabular neural networks) then we should be able to create reasonably accurate models that are reasonably straight forward to explain.

Thanks :)

Yep, code's on GitHub. I'm more than happy to collaborate, there's heaps of things that need to be done.