Fitting a logistic regression model
In addition to regression models, the parsnip
package also provides a general interface to classification models in R.
In this exercise, you will define a parsnip
logistic regression object and train your model to predict canceled_service
using avg_call_mins
, avg_intl_mins
, and monthly_charges
as predictor variables from the telecom_df
data.
The telecom_training
and telecom_test
tibbles that you created in the previous lesson have been loaded into this session.
This exercise is part of the course
Modeling with tidymodels in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Specify a logistic regression model
logistic_model <- ___ %>%
# Set the engine
___ %>%
# Set the mode
___
# Print the model specification
logistic_model