I wrote the open-source interpretable modelling library Durkon from scratch, using nothing more complicated than NumPy and Pandas. Because it’s my own work, I know exactly how to modify and extend it to fit your organisation’s unique needs.
"Hugh is the best I've ever worked with at certain invaluable things: he thinks from first principles and covers all of the many ways that ML training and deployment goes wrong. I've seen him implement unprecedented techniques by hand, all the while watching for the actual business effect behind the metrics." Gavin Leech, AI Researcher, cofounder of Arb Research
All Durkon models can be represented as a collection of Partial Dependency Plots and/or Relativity Tables. This means the decisions they make will always be easy to explain to your clients / customers / regulators / underwriters / superiors / subordinates / self. (Interactive example graphs are here.)
"We've used multiple Durkon models in our model stack, both for legible uses internally, but also as live models - they are both quicker to build and more accurate than [handmade] GLM models. Hugh's support to build and deploy the models, as well as understanding business problems to update and improve on the model features, has been second to none." Aled Price, Head of Pricing, By Miles
I am that most precious of things, an engineer who knows how to communicate. I wrote everything on this website; I’ll write the documentation for my work in a similar style and to a similar standard.
"Hugh is very good at using words and images to explain technical concepts in clear and engaging ways." Dr Jessica Rumbelow (née Cooper), AI Researcher
- A dataset.
- Details of the model you’d need.
- A model built using that dataset.
- Confidence that Explicability is the right choice for your business.
- Whatever you gave WTW and/or your Emblem modelling team.
- Whatever they gave you in return.
- A better model.
- A detailed explanation of everything wrong with your current model.
- . . . or your money back!
- Someone who knows Python, NumPy, and Pandas to an acceptable level.
- Someone who knows everything they need to know about Durkon modelling.
- Your current model’s predictions on a training dataset.
- A list of features I should use for adjustments.
- A list of suggested adjustments.
- A list of things you want the model (not) to do.
- A model which does(n’t) do those things.
- Guidelines for how to make the tradeoff between performance and feature count (“use the 15 best features”, “use as few externally-sourced features as possible”, etc).
- A model built using those guildelines.
- A frankly inexplicable level of faith in my integrity.
- An XGB model which does the same thing my legible model does.
- Comparisons between the Durkon and XGB models on key performance metrics.
- Data from X years ago.
- Data from 2*X years ago.
- Ideally, data from 3*X and 4*X years ago.
- A model which avoids systematic biases towards extreme predictions when predicting in today’s context.
- A dataset where the response variable is predictable enough from the explanatory variables that error modelling is feasible.
- An error-predicting model which predicts how accurate the main model will be for each row.
- A dataset with censored response variables.
- An indication of which records are censored and how.
- A model which makes accurate and unbiased predictions despite that censorship.
- An example of the target model format.
- A Durkon model expressed in that model format.
- A tool for converting Durkon models to that model format.
- A new challenge.
- Creativity, ingenuity, and dedication.
*To see why this is true, consider the pathological case where changes in context are so extreme that your model is uncorrelated with reality: if you guess people’s heights randomly, the people you expect to be tall will be on average shorter than you expect, and the people you expect to be short will be on average taller than you expect.
**For example, when modelling market trends in sealed-bid first-place auctions if you only have the winning bids and are already part of the market.
Somehow, not everyone who goes to the trouble of making an explicable model finds time to explicate it. If you already have a transparent model – i.e., anything built using Excel or Emblem – I can look through it for you, and produce a list of suboptimalities / potential regulatory issues / just-plain-weirdness for you to address.
I make Data Science challenges and release them for free online. This is more of a hobby than a service, but if you offered me large amounts of money to write challenges tailored to your requirements I wouldn’t say no.
I like editing things, and I’m pretty good at it. If you want me to look through a document for mistakes – grammatical, stylistic, factual, logical, strategic, or moral – I’d charge very reasonable rates. (. . . by Data Science standards, at least.)