शुरू करेंमुफ़्त में शुरू करें

Create bagged trees

Ensemble models like bagged trees are more powerful than single decision trees. Each tree in the ensemble gives a vote, and the average or majority vote is your prediction. This ensures you use swarm intelligence instead of relying on a single tree. For bagged trees, the bootstrap method ensures that in every ensemble tree, only a bootstrapped sample (sampled with replacement) of the original dataset is used to train the tree and create the prediction.

Put this into practice by building a bagged classification tree yourself!

The credit card customers training data is pre-loaded as customers_train.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Machine Learning with Tree-Based Models in R

पाठ्यक्रम देखें

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

# Create the specification
library(baguette)

spec_bagged <- ___ %>%
  ___ %>%
  ___(___, ___)

spec_bagged
कोड संपादित करें और चलाएँ