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4 The Decision Tree Learning Algorithm 4.1 Issues in learning a decision tree How can we build a decision tree given a data set? First, we need to decide on an order of testing the input features. Next, given an order of testing the input features, we can build a decision tree by splitting the examples whenever we test an input feature.
Learning algorithm. Induction. Learn Model. Model. Apply Model. Deduction. Intuition behind a decision tree. Ask a series of questions about a given record. Each question is about one of the attributes. Answer to one question decides what question to ask next (or if a next question is needed)
Decision Tree. Figure 1: Decision Tree Example. From the example in Figure 1, given a new shape, we can use the decision tree to predict its label. 1.4 Expressivity. all Boolean functions can be expressed as linear functions. Decision trees can be thought of as a disjunction of conjunctions, or rewritten as rules in Disjunctive Normal Form (DNF).
Decision Trees. Simple but powerful learning algorithm. One of the most widely used learning algorithms in Kaggle competitions. Lets us introduce ensembles, a key idea in ML more broadly. Useful information theoretic concepts (entropy, mutual information, etc.)
Decision Trees. CS229: Machine Learning. Carlos Guestrin. Stanford University. Slides include content developed by and co-developed with Emily Fox. Predicting potential loan defaults. What makes a loan risky? I want a to buy a. Credit history explained. Did I pay previous loans on time? Example: excellent, good, or fair. Credit History. ★★★★.
How to build a decision tree: Start at the top of the tree. Grow it by \splitting" attributes one by one. To determine which attribute to split, look at \node impurity." Assign leaf nodes the majority vote in the leaf. When we get to the bottom, prune the tree to prevent over tting Why is this a good way to build a tree? 1
1.10. Decision Trees # Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.
Simple but powerful learning algorithm. One of the most widely used learning algorithms in Kaggle competitions. Lets us introduce ensembles, a key idea in ML. Useful information theoretic concepts (entropy, mutual information, etc.) Decision Trees. Decision trees make predictions by recursively splitting on di erent attributes according to. a.
For decision trees, we will especially focus on discrete features (though continuous features are possible, see end of slides) Example: mushrooms. http://www.usask.ca/biology/fungi/ Mushroom features. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s . cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s .
1. Decision Tree Learning. CS4780 – Machine Learning Fall 2009. Thorsten Joachims Cornell University Reading: Mitchell Sections 2.1, 2.2, 2.5-2.5.2, 2.7, Chapter 3. Outline. •Hypothesis space. •Version space. •Inductive learning hypothesis. •List-then-eliminate algorithm. •Decision tree representationDecision tree representation.
