knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The explore package offers a simplified way to use machine learning and make a prediction.
explain_tree()
creates a decision treeexplain_forest()
creates a random forestexplain_xgboost()
creates a xgboost modelexplain_logreg()
creates a logistic regressionpredict_target()
uses a model to make a predictionWe use synthetic data in this example
library(dplyr) library(explore) train <- create_data_buy(obs = 1000, seed = 1) glimpse(train)
First we create a decision tree model, using buy
as target (buy
contains only 0 and 1 values)
train %>% explain_tree(target = buy)
We see some clear patterns. Now we create a random forest model (as it is more accurate).
To get the model itself, use parameter out = "model"
model <- train %>% explain_forest(target = buy, out = "model")
Now we create test data and use the model for a prediction. We use a different seed so we get different data.
test <- create_data_buy(obs = 1000, seed = 2) glimpse(test)
test <- test %>% predict_target(model = model) glimpse(test)
Now we got 2 new variables prediction_0
(the probability of buy == 0
) and
prediction_1
(the probability of buy == 1
). We can check the predictions by comparing prediction_1
with real values of buy.
test %>% explore(prediction_1, target = buy)
There is a clear difference between buy == 0
and buy == 1
. So the prediction works.
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