See the previous installment in this series for Setup and Download.
Below is a peak into what that data looks like. You can get results directly from the returned
Scan() object but I prefer to read back the csv and analyze that since I’d like to be able to monitor results as the come in (different process) and be able to evaluate in future w/o re-running the Scan.
Note: Since Log-Loss values are all so tightly distributed, I’m creating a vallossimprovement metric which is the improvement relative to naive baseline (always estimating 50% probability). For this metric, higher is better.
We could simply take the top result and call it good. However, I think the real value here is to gain understanding about how parameters affect results. Further it’s very unlikely that we’ve happened to find the global maximum with our starting values.
Below, I’ll start by running several univariate measures to see what the impact of each factor appears to be, in isolation:
As a total aside, we can also measure relationships which all assume, but may not know for certain. Below, I’ve plotted the number of epochs before early stopping (when the model begins to overfit and get worse on validation) vs. network size. Unsurprisingly, it takes larger networks longer to converge. The relationship seems pretty close to logarithmic relative to “links” between nodes.In :
Visit The Alpha Scientist blog to download the complete code:
The Alpha Scientist blog – Chad is a full-time quantitative trader who has been working on data analytics since before it was cool. He has long balanced his interest in computer science (MS in EE/CS from MIT) with a fascination in markets (CFA designation in 2009). Prior to becoming a full-time quant, he built analytics products and managed teams at software companies across Silicon Valley. If you’ve found this post useful, please follow @data2alpha on Twitter and forward to a friend or colleague who may also find this topic interesting. https://alphascientist.com/
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