Optimize
your models.

Optimizing a model is a cyclic process. You need to test the model’s performance with a range of parameter settings, feature sets, and different versions of the code.

Additionally, you may try multiple versions of the training data, as that data is being updated or cleaned.

After you’ve run the model and learned how it performed, you can record the performance metrics, update the parameters, debug the code, and then start over again.

Typically, this loop can repeat tens or hundreds of times before you find an optimal model version.

Keep track of
all the moving parts.

Dotscience keeps track of all of these moving parts automatically, so that everything you and your collaborators do is reproducible.

Dotscience uses this information to generate visualizations showing how changes to parameters affect your model’s performance, so you can better understand your model’s behaviour and get quick insights into the next set of changes to try.

Sign up for our beta

Our beta testers get early access to dotscience. Not only can you help shape the product, you can start using it right now in your workflows.

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