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now that you know how to work with list columns in a tidy manner you can begin to work with the tools you need to explore and evaluate machine learning models as you can probably imagine the bulk of the work of machine learning resides in step two of this workflow since you can store complex model objects in your data frame you can also work with these objects using the tools available in various our packages in this video we will focus on the broom package a package designed to convert useful model outputs into tidy data frames the core of broom is encapsulated by three functions which aim to extract conceptually different information from any model tidy is used to extract the statistical findings of a model Glantz provides a one row summary of a model and augment appends the predicted values of a model to the data being modeled letamp;#39;s explore each of these in greater detail by reviewing the results of the linear model that you created for Algeria if you look at the summary of
