Back to the AI lecture. Installed a new module:
* * *
Left-off with the issue of loss functions to evaluate our model. Loss1 shows
a simple count of cases that the model doen't pick up. L2 penalizes a large
discrepency that is not picked up:
Overfitting the data can sometimes lead to a model that describes the data perfectly,
but is a weak predictor for that very reason.
The way around this problem is to add a complexity element, whose value we will
choose to weed out too precise fits.
One can easily enough divide our data between a learning set and a testing set.
Best to do this in multiple configurations.
Python has libraries that will handle all this for us. This is sklearn I have installed.
Working with data on serial numbers from US bank notes, and knowing which are
genuine and which are counterfeit, one can see how the different models perform:
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