Machine Learning and Tidymodel Model setting, {Parsnip} Rpackage Parsnip standardizes model specification. Tidymodel follows the concept of lazy evaluation of the tidyverse. Parsnip sets unified specifications and lately evaluates.
Feature engineering, {Recipes} Recipes make preprocessing easy with step_() functions. Recipes after specification calculate.
Resampling, {rsample} To choose a model and hyperparameters, we must validate the different models.
Making hyperparameter set, {dials} The Rpackage {dials} set hyperparameter similarily with {Parsnip}.
This is a note of applied machine learning workshop RStudion conference 2020
Why is it hard to predict (domain knowledge).
purrr::map allows inline code.
purrr::map and tidyr::nest covered because they are used in resample or tune.
Skew data might be looking outlier.
People look at data in many different ways like outliers, missingness, correlation, and suspicion of an important variable.
The ggplot is good to explore variables adding geoms changing plot.