What is the main purpose of using cross-validation when training a Decision Tree?
Practice Questions
1 question
Q1
What is the main purpose of using cross-validation when training a Decision Tree?
To increase the size of the training set
To tune hyperparameters
To assess the model's generalization ability
To visualize the tree structure
Cross-validation helps in assessing how the model will generalize to an independent dataset, thus providing a better estimate of its performance.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main purpose of using cross-validation when training a Decision Tree?
Solution: Cross-validation helps in assessing how the model will generalize to an independent dataset, thus providing a better estimate of its performance.
Steps: 6
Step 1: Understand that when we train a Decision Tree, we want to know how well it will perform on new, unseen data.
Step 2: Realize that if we only test the model on the same data it was trained on, we might get an overly optimistic view of its performance.
Step 3: Learn that cross-validation is a technique that helps us test the model on different subsets of the data.
Step 4: Know that in cross-validation, we split the data into several parts (or folds).
Step 5: Train the model on some parts and test it on the remaining parts multiple times.
Step 6: Collect the performance results from each test to get a better overall estimate of how the model will perform on new data.
