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  3. Machine Learning in the Tidyverse
  • 1

    Foundations of "tidy" Machine learning

    Free

    This chapter will introduce you to the backbone of machine learning in the tidyverse, the List Column Workflow (LCW). The LCW will empower you to work with many models in one dataframe.
    This chapter will also introduce you to the fundamentals of the broom package for exploring your models.

  • 2

    Multiple Models with broom

    This chapter leverages the List Column Workflow to build and explore the attributes of 77 models. You will use the tools from the broom package to gain a multidimensional understanding of all of these models.

  • 3

    Build, Tune & Evaluate Regression Models

    In this chapter you will learn how to use the List Column Workflow to build, tune and evaluate regression models. You will have the chance to work with two types of models: linear models and random forest models.

  • 4

    Build, Tune & Evaluate Classification Models

    In this chapter you will shift gears to build, tune and evaluate classification models.

Initializing

Exercise

Explore your best and worst fitting models

Let's explore your four best and worst fitting models by comparing the fitted lines with the actual values.

Instructions 1/2

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  • 1

    Visualize the fit of your four best fitting models with respect to year by plotting both life_expectancy as points and .fitted as a line.

  • 2

    Visualize the fit of your four worst fitting models with respect to year by plotting both life_expectancy as points and .fitted as a line.