Statistical Learning: 8.R.1 Fitting Trees

TL;DR

This content explores the use of decision trees and random forests in R for data analysis and prediction, discussing the process of building, pruning, and evaluating these models.

Transcript

okay here we are today we're going to look at trees and lots of trees we're going to look at decision trees and then later on we're going to look at random forests and we're going to see how to fit these in r and just as before we're using our studio and so here we are we've got an r studio session and we're going to use our markdown and we'll star... Read More

Key Insights

• 🌲 Decision trees and random forests are useful techniques for data analysis and prediction in R.
• 🌲 Decision trees can be fitted, visualized, and pruned to avoid overfitting.
• 😫 The process of splitting data into training and test sets helps evaluate model performance.
• 😵 Cross-validation is helpful in determining the optimal pruning level for decision trees.
• 🌲 Pruning decision trees results in shallower trees that are easier to interpret.

Questions & Answers

Q: What is the purpose of converting the sales variable into a binary variable called "high"?

The purpose of converting the sales variable into a binary variable is to demonstrate the use of decision trees with a binary response. Categories of "high" and "not high" are created based on a threshold value of 8 for sales.

Q: How are decision trees pruned to avoid overfitting?

Decision trees can be pruned by using cross-validation and selecting a suitable cost complexity parameter. This parameter controls the trade-off between tree complexity and fitting the training data, helping to prevent overfitting.

Q: What is the advantage of using if else statements in creating binary variables?

If else statements offer convenience in creating binary variables based on specific conditions. In this case, the if else statement is used to assign the label "high" or "not high" to the binary variable based on whether sales is above or below a threshold.

Q: How does cross-validation help in deciding the optimal pruning level for a decision tree?

Cross-validation helps in deciding the optimal pruning level by measuring the performance of different tree sizes on unseen data. It calculates the misclassification error for different pruning levels, allowing the selection of the tree size with the lowest error rate.

Summary & Key Takeaways

• The content begins by loading the necessary packages and working with car seat data, converting a quantitative variable into a binary variable called "high" using the if else construct.

• A decision tree model is then fitted using the new binary variable, excluding the original sales variable. The summary of the model and a plot of the tree are generated.

• The content explores pruning the tree to avoid overfitting, and a detailed version of the pruned tree is printed to show the details of each terminal node.

• The car seat data is split into a training and test set, and the model is refitted using the training set. Predictions are made on the test set, and the error rate is evaluated.

• Cross-validation is used to optimally prune the tree, and the results show the deviance and cost complexity parameter for each pruning step.

• The pruned tree is plotted and evaluated on the test set again, resulting in a shallower tree that is easier to interpret.