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
- someone has some dataset, and based on that dataset, a model was fitted. But we cannot see how it works….
- we can generate “fake data”, feed the model with those data, and get predictions
- based on those predictions, we wish we can fit a model that should be close to the the ‘true’ model used
- 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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