Algorithms: Decision Trees
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1 Algorithms: Decision Trees
2 A small dataset: Miles Per Gallon Suppose we want to predict MPG From the UCI repository
3 A Decision Stump
4 Recursion Step Records in which cylinders = 4 Records in which cylinders = 5 Take the Original Dataset.. And partition it according to the value of the attribute we split on Build Tree from these Records Records in which cylinders = 6 Records in which cylinders = 8
5 Second level of tree Recursively build a tree from the seven records in which there are four cylinders and the maker was based in Asia (Similar recursion in the other cases)
6 The final tree
7 Classification of a new example Classifying a test example Traverse tree Report leaf label
8 Learning decision trees is hard!!! Learning the simplest (smallest) decision tree is an NPcomplete problem [Hyafil & Rivest 76] Resort to a greedy heuristic: Start from empty decision tree Split on next best attribute (feature) Recurse How to choose the best attribute and the value for a split?
9 Entropy Entropy characterizes our uncertainty about our source of information More uncertainty, more entropy! Information Theory interpretation: H(Y) is the expected number of bits needed to encode a randomly drawn value of Y (under most efficient code)
10 Information gain Advantage of attribute decrease in uncertainty Entropy of Y before you split Entropy after split Weight by probability of following each branch, i.e., normalized number of records Information gain is difference
11 Learning decision trees Start from empty decision tree Split on next best attribute (feature) Use, for example, information gain to select attribute Split on Recurse
12 A Decision Stump
13 Base Cases Base Case One: If all records in current data subset have the same output then don t recurse Base Case Two: If all records have exactly the same set of input attributes then don t recurse
14 Base Case 1 Don t split a node if all matching records have the same output value
15 Base Case 2 Don t split a node if all records have exactly the same set of input attributes
16 Basic Decision Tree Building Summarized BuildTree(DataSet,Output) If all output values are the same in DataSet, return a leaf node that says predict this unique output If all input values are the same, return a leaf node that says predict the majority output Else find attribute X with highest Info Gain Suppose X has n X distinct values (i.e. X has arity n X ). Create and return a non-leaf node with n X children. The i th child should be built by calling BuildTree(DSi,Output) Where DSi built consists of all those records in DataSet for which X = ith distinct value of X.
17 Decision trees will overfit Standard decision trees are have no learning biased Training set error is always zero! (If there is no label noise) Lots of variance Will definitely overfit!!! Must bias towards simpler trees Many strategies for picking simpler trees: Fixed depth Fixed number of leaves Or something smarter
18 Consider this split
19 A statistical test Suppose that mpg was completely uncorrelated with maker. What is the chance we d have seen data of at least this apparent level of association anyway?
20 Using to avoid overfitting Build the full decision tree as before But when you can grow it no more, start to prune: Beginning at the bottom of the tree, delete splits in which have extreme low chance to appear//pchance > MaxPchance Continue working your way up until there are no more prunable nodes
21 What you need to know about decision trees Decision trees are one of the most popular data mining tools Easy to understand Easy to implement Easy to use Computationally cheap (to solve heuristically) Information gain to select attributes (ID3, C4.5, ) Presented for classification, can be used for regression and density estimation too Decision trees will overfit!!! Zero bias classifier! Lots of variance Must use tricks to find simple trees, e.g., Fixed depth/early stopping Pruning
22 Decision trees in Matlab Use classregtree class Create a new tree: t=classregtree(x,y), X is a matrix of predictor values, y is a vector of n response values Prune the tree: tt = prune(t, alpha, pchance) alpha defines the level of the pruning Predict a value y= eval(tt, X)
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