Tag Archives: modeling

Reverse Engineering with Correlated Features

In econometric modeling, I usually have a problem with correlated features. A few weeks ago, I was discussing feature selection when features are correlated. This week, I was wondering about reverse engineering when features might be correlated (not to say very correlated). The way I see reverse engineering is the following

  1. someone has some dataset, and based on that dataset, a model was fitted. But we cannot see how it works….
  2. we can generate “fake data”, feed the model with those data, and get predictions
  3. based on those predictions, we wish we can fit a model that should be close to the the ‘true’ model used
  4. one way to measure how good our model is is to compare predictions on the initial data with our model with the original dataset (or the initial ‘true’ values if we use generated datasets).

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“Improving Segmentation” (using Lorenz curves, or sort of)

This afternoon, André did send me an interesting graph about the use of Lorenz curve in the context of insurance pricing (and modeling)

It is some sort of Lorenz curve, upside-down, with on the x-axis the proportion of the population, and on the y-axis the proportion of the losses. The important point is that the population is sorted according the their risk, i.e. their premium. The code to generate such a curve is actually quite simple,

L <- function(u,varx="premium",vary="losses"){
vv=Vectorize(function(u) L(u))(vu)

My concern was more on two labels on the figure, with on the top-left “perfect pricing” and on the first diagonal “average pricing“. What could that possibly mean? Is there even such a thing as a “perfect pricing“? In order to understand what we plot here, let us generate some dataset, and fit some model. Including things that might be seen as the “perfect model“: the price base on the parameters used to generate the data, and the model used to generate the data, fitted on the data.

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Predictive Modeling










X=Davis$height / 30.48






We will also discuss random forrests, bagging and boosting techniques

pdf version of the slides can be downloaded.

SOA Webinar on Predictive Modeling
