THIS IS A COMPLETE NOTE FROM ALL BOOKS + LECTURE!
Save your time for internships, other courses by studying over this note!
Are you a 1st/2nd year of Business Analytics Management student at RSM, who want to survive the block 2 Machine Learning module? Are you overwhelmed with 30 pages of re...
ISLR ８: Tree-Based Methods
Tree-based methods for regression and classification involve stratifying/segmenting the
predictor space into a number of simple regions.
For prediction, we use mean/mode response value for the training observations in the region
to which they belong.
Decision tree: the set of splitting rules to segment the predictor summarized in a tree
8.1: The basics of decision trees
8.1.1 Regression trees
In the tree analogy, decision trees are typically drawn upside down.
Splitting rule: starting at the top of the tree.
For every observation fall into each region, we make the same prediction: mean of the response
values for the training observation in each region
Decision tree: Superfical similarities with KNN
- K-NN is expensive because each prediction requires a comparison to entire training set.
- What is we partitioned the training set ahead of time, summarized the partitions with
simple rules, and pre-calculated predictions?
Purpose
Partition feature space into regions where values of target variables are relatively
homogeneous(or pure if classification tree case)
Decision tree terminologies
Terminal nodes/leaves = Regions: where all observations end up.
Internal nodes = Points along the tree where the predictor space is split
- The first internal node with the most important factor to determine the response factor
, Value at the top in the square = predicted y value
Advantage:
- Interpretability
- Similarity to human decisions
- No dummy variable required
- Good “Base learners”
- It can be visually and intuitively displayed
- Easy to handle qualitative predictors without creating dummy variables
- Can deal with the non-linear relationship by digging into specific features multiple times
in splitting
Disadvantage
- Not competitive in terms of accuracy (aggregation of trees can improve it)
- Trees can be very non-robust: small change in the data can cause a large change in the
final estimated tree
Process: Greedy approach
It is computationally infeasible to consider every possible partition of the feature space into J
boxes.
In creation of the decision tree, we take top-down greedy approach/recursive binary
splitting (for decision tree case)
o It begins at the top of the tree and then successively splits the predictor space
with two new branches
o It is greedy as the best split is made at the each of the split rather than looking
ahead and picking one that lead to a better tree in some future step.
Example: predicting salary of baseball players
1. Years <4.5?
2. For players with years <4.5, find the mean response value for those players (e.g. mean
log salary of 5.107 = $165,174)
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller ArisMaya. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $11.92. You're not tied to anything after your purchase.
