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Model Performance in Data Science Live Book

Hi there! I decided to almost re-write the model validation section since it didn't reflect real case scenarios.

Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects on model validation through classical cross-validation, bootstrapping, and going further in the nature of the error. And also take advantage of validation when data is time dependent.


Error on predictive models Chapter: Knowing the Error



Error on predictive models Chapter: Out-of-Time Validation

There is a lot more to tell about model validation, but it's a kick start.

Coming soon, there will be an update on methodological aspects in data preparation.


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First published at: http://blog.datascienceheroes.com/model-performance-in-data-science-live-book


Pabloc

Data Analysis ~ The art of finding order in data by browsing its inner information.