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Data Science with Python and Dask by Jesse Daniel

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10 Machine learning with Dask-ML

This chapter covers

  • Building machine learning models using the Dask-ML API
  • Using the Dask-ML API to extend scikit-learn
  • Validating models and tuning hyperparameters using cross-validated gridsearch
  • Using serialization to save and publish trained models

A common admission by data scientists is that the 80/20 rule definitely applies to data science: that is, 80% of time spent on data science projects is preparing data for machine learning and the other 20% is actually building and testing the machine learning models. This book is no exception! By now, we’ve been through the gathering, cleaning, and exploration process for two different datasets in two different “flavors”—using DataFrames and using Bags. It’s ...

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