Unwittingly obfuscating the fact that you're not doing AI

https://breakitdownto.earth/2019/06/06/Obfuscating_a_lack_of_AI.html

by s_Hogg • 7 years ago

85 11 7 years ago

ageitgey• 7 years ago

This is great advice. Another related issue I see is when you engineer a new feature that can't always be 100% accurate because the source data is spotty but you intuitively think the new festure should help the classifier anyway when it is present. And if the new feature's feature importance in the trained model turns out really high, you think you've done something great. But in the end you made model that simply detects the presence of your new feature which you knew wasn't 100% accurate anyway because the source data it is derived from is spotty. So you've accomplished precisely nothing.

s_Hogg3• 7 years ago

OP Here, glad you liked it!

The thing you're talking about definitely happens heaps as well, because of a fundamental mental blind spot we have. I'd definitely love to hear if you've got any more stories along these lines. The psychology of what makes a successful machine learning project really interests me, and I don't mean in terms of platitudes about openness and transparency.

I'm really tempted to write another post about specifically the sort of thing you talk about in your example - narrative fallacies in machine learning. Basically because we operate in the unknown we tend to want to string the evidence we have together in a nice appealing way.

thetrainfold• 7 years ago

It would be unusual not to check the performance of the model at predicting the target variable, which would validate whether or not the derived feature is useful.

privong3• 7 years ago

I think this is minor, but I noticed the example doesn't use error bars for the customer numbers. The customer counts in the product/churn categories are counting statistics, so have an associated Poisson uncertainty. My guess is that considering the uncertainties when doing the likelihood parameter estimation won't fully obviate this issue, but I wonder how much it would help. I'm also not sure if real-world implementations commonly consider such uncertainties on their measured metrics.

s_Hogg3• 7 years ago

Hi - the reason there are no error bars for the number of customers in that 2x2 table is because there is no associated uncertainty. Those numbers are as you might find them in a dataset from summing with this particular problem. The problem we're looking at here is a binary churn/no churn problem, as opposed to one that looks at how many people churn out of a population.

That said, that absolute lack of uncertainty is itself the problem. The Maximum Likelihood approach to this sort of modelling implicitly assumes that the data you have is all there is to know about the problem you're working on and so can very easily overfit on a weird artefact like that. If you want to incorporate some kind of uncertainty in your estimates, then you either need to augment your dataset (i.e. include some random examples, roughly speaking), or estimate your model using the Bayesian approach which explicitly allows for uncertainty relating to the data itself.

Hope this clarifies!

privong3• 7 years ago

Those numbers are as you might find them in a dataset from summing with this particular problem. The problem we're looking at here is a binary churn/no churn problem, as opposed to one that looks at how many people churn out of a population.

But isn't that still a count per bin? Since it's a count it has a Poisson uncertainty.

f you want to incorporate some kind of uncertainty in your estimates, then you either need to augment your dataset (i.e. include some random examples, roughly speaking), or estimate your model using the Bayesian approach which explicitly allows for uncertainty relating to the data itself.

Or use a likelihood that considers uncertainties. That also allows one to explicitly consider the uncertainties when maximizing the likelihood.

tictacttoe• 7 years ago

Well the Poisson uncertainty is only reasonable if the count is sampling a random process. But yea, they could try adding Poisson uncertainties and using a probabilistic emulator like a Gaussian process.

_bxg1• 7 years ago

There was an article on here a few months back that said something like, "The majority of today's applications of AI could be just as well - if not better - served by a simple heuristic"

ksaj• 7 years ago

I thought it was "database." Or maybe I'm projecting. A lot of "AI" project sales blurbs I've seen suggest they are really just databases with a pretty search function. A number of Knowledge Bases fit this example.

Unwittingly obfuscating the fact that you're not doing AI

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