Generate a tuning grid
The standard hyperparameters of most models provide a good fit for most datasets. Yet, they need optimization for the best performance. Otherwise, it's like driving a car with an activated hand brake. Release the brake and tune your models!
In this exercise, you'll create two objects that serve as a starting point: a tuning grid (a set of hyperparameter combinations) and a model specification that you later train with every value of the grid.
แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร
Machine Learning with Tree-Based Models in R
แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ
ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์
# Create a specification with tuning placeholders
tune_spec <- decision_tree(___ = ___,
___ = ___) %>%
# Specify mode
___ %>%
# Specify engine
___
print(tune_spec)