Crack the matrix
Visual representations are a great and intuitive way to assess results. One way to visualize and assess the performance of your model is by using a confusion matrix. In this exercise, you will create the confusion matrix of your predicted values to see in which cases it performs well and in which cases it doesn't.
The result of the previous exercise, predictions_combined, is still loaded.
แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร
Machine Learning with Tree-Based Models in R
แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ
ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์
# The confusion matrix
diabetes_matrix <- ___(___,
___,
___)
# Print the matrix
___