hai_xgboost_data_prepper(.data, .recipe_formula)Introduction
Sometimes we may want to quickly format some data in order to just pass it through some algorithm just to see what happens, how crazy are things, just to get an idea of what may lie ahead…a lot of prep.
With my r package {healthyR.ai} there is a set of prepper functions that will automatically do a ‘best effort’ to format you data to be used in the algorithm you choose (should it be supported).
Today we will talk about [hai_xgboost_data_prepper()
Function
Let’s take a look at the function call.
Now let’s go over the arguments that are passed to the function.
.data- The data that you are passing to the function. Can be any type of data that is accepted by the data parameter of the recipes::reciep() function..recipe_formula- The formula that is going to be passed. For example if you are using the diamonds data then the formula would most likely be something like price ~ .
Example
Let’s go over some examples.
library(ggplot2)
library(healthyR.ai)
# Regression
hai_xgboost_data_prepper(.data = diamonds, .recipe_formula = price ~ .)Recipe
Inputs:
role #variables
outcome 1
predictor 9
Operations:
Factor variables from tidyselect::vars_select_helpers$where(is.charac...
Novel factor level assignment for recipes::all_nominal_predictors()
Dummy variables from recipes::all_nominal_predictors()
Zero variance filter on recipes::all_predictors()reg_obj <- hai_xgboost_data_prepper(diamonds, price ~ .)
get_juiced_data(reg_obj)# A tibble: 53,940 × 27
carat depth table x y z price cut_1 cut_2 cut_3 cut_4 cut_5
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.23 61.5 55 3.95 3.98 2.43 326 0.359 -0.109 -0.522 -0.567 -0.315
2 0.21 59.8 61 3.89 3.84 2.31 326 0.120 -0.436 -0.298 0.378 0.630
3 0.23 56.9 65 4.05 4.07 2.31 327 -0.359 -0.109 0.522 -0.567 0.315
4 0.29 62.4 58 4.2 4.23 2.63 334 0.120 -0.436 -0.298 0.378 0.630
5 0.31 63.3 58 4.34 4.35 2.75 335 -0.359 -0.109 0.522 -0.567 0.315
6 0.24 62.8 57 3.94 3.96 2.48 336 -0.120 -0.436 0.298 0.378 -0.630
7 0.24 62.3 57 3.95 3.98 2.47 336 -0.120 -0.436 0.298 0.378 -0.630
8 0.26 61.9 55 4.07 4.11 2.53 337 -0.120 -0.436 0.298 0.378 -0.630
9 0.22 65.1 61 3.87 3.78 2.49 337 -0.598 0.546 -0.373 0.189 -0.0630
10 0.23 59.4 61 4 4.05 2.39 338 -0.120 -0.436 0.298 0.378 -0.630
# … with 53,930 more rows, and 15 more variables: color_1 <dbl>, color_2 <dbl>,
# color_3 <dbl>, color_4 <dbl>, color_5 <dbl>, color_6 <dbl>, color_7 <dbl>,
# clarity_1 <dbl>, clarity_2 <dbl>, clarity_3 <dbl>, clarity_4 <dbl>,
# clarity_5 <dbl>, clarity_6 <dbl>, clarity_7 <dbl>, clarity_8 <dbl># Classification
hai_xgboost_data_prepper(Titanic, Survived ~ .)Recipe
Inputs:
role #variables
outcome 1
predictor 4
Operations:
Factor variables from tidyselect::vars_select_helpers$where(is.charac...
Novel factor level assignment for recipes::all_nominal_predictors()
Dummy variables from recipes::all_nominal_predictors()
Zero variance filter on recipes::all_predictors()cla_obj <- hai_xgboost_data_prepper(Titanic, Survived ~ .)
get_juiced_data(cla_obj)# A tibble: 32 × 7
n Survived Class_X2nd Class_X3rd Class_Crew Sex_Male Age_Child
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 No 0 0 0 1 1
2 0 No 1 0 0 1 1
3 35 No 0 1 0 1 1
4 0 No 0 0 1 1 1
5 0 No 0 0 0 0 1
6 0 No 1 0 0 0 1
7 17 No 0 1 0 0 1
8 0 No 0 0 1 0 1
9 118 No 0 0 0 1 0
10 154 No 1 0 0 1 0
# … with 22 more rowsVoila!