Every data scientist is conversant in experimentation.
You already know the drill. You get a dataset, load it right into a Jupyter notebook, explore it, preprocess the information, fit a baseline model or two, after which train an initial final model, comparable to XGBoost. The primary time around, perhaps you don’t tune the hyperparameters and include 20 features. Then, you check your error metrics.
They appear okay, but perhaps your model is overfitting a bit. So you choose to tune some regularization parameters (eg max depth) to scale back the complexity of the model and run it again.
You see a bit of improvement from the last run, but perhaps you wish to also:
- Add more features
- Perform feature selection and take away some features
- Try a special scaler in your features
- Tune different/more hyperparameters
As the several sorts of tests you wish to run increases, the tougher it’s to recollect which mixtures of your “experiments” actually yielded the perfect results. You’ll be able to only run a notebook so over and over, print out the outcomes, and replica/paste them to a Google doc before you get frustrated.
That is where experiment tracking is available in.
As I discussed in my article about becoming an ideal data scientist, having a proper option to track your experiments will make your life so much easier and your results much clearer.
In this text I’ll be walking you thru tips on how to arrange an experiment using Neptune.ai, which permits you to run experiments on 1 project without cost and can assist you to get conversant in the method. There are many other great experiment tracking tools on the market, but since I’m essentially the most conversant in Neptune that’s the one I’ll be basing this guide off of. This is just not promotional in any way — I just wish to showcase what experiment tracking looks like in Python and am using Neptune as my tool of alternative.
After you’ve pip installed Neptune and arrange your Jupyter notebook environment, you’ll must link your notebook to the…
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