From decision trees to random forests
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This document discusses decision trees and random forests for classification problems. It explains that decision trees use a top-down approach to split a training dataset based on attribute values to build a model for classification. Random forests improve upon decision trees by growing many de-correlated trees on randomly sampled subsets of data and features, then aggregating their predictions, which helps avoid overfitting. The document provides examples of using decision trees to classify wine preferences, sports preferences, and weather conditions for sport activities based on attribute values.
![Motivation
• Training sample of points
covering area [0,3] x [0,3]
• Two possible colors of
points](https://image.slidesharecdn.com/randomforests-150902071011-lva1-app6891/85/From-decision-trees-to-random-forests-35-320.jpg)
From decision trees to random forests
- 1. From decision trees to random forests Viet-Trung Tran
- 2. Decision tree learning • Supervised learning • From a set of measurements, – learn a model – to predict and understand a phenomenon
- 3. Example 1: wine taste preference • From physicochemical properties (alcohol, acidity, sulphates, etc) • Learn a model • To predict wine taste preference (from 0 to 10) P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis, Modeling wine preferences by data mining from physicochemical proper@es, 2009
- 4. Observation • Decision tree can be interpreted as set of IF...THEN rules • Can be applied to noisy data • One of popular inductive learning • Good results for real-life applications
- 5. Decision tree representation • An inner node represents an attribute • An edge represents a test on the attribute of the father node • A leaf represents one of the classes • Construction of a decision tree – Based on the training data – Top-down strategy
- 6. Example 2: Sport preferene
- 7. Example 3: Weather & sport practicing
- 8. Classification • The classification of an unknown input vector is done by traversing the tree from the root node to a leaf node. • A record enters the tree at the root node. • At the root, a test is applied to determine which child node the record will encounter next. • This process is repeated until the record arrives at a leaf node. • All the records that end up at a given leaf of the tree are classified in the same way. • There is a unique path from the root to each leaf. • The path is a rule which is used to classify the records.
- 9. • The data set has five attributes. • There is a special attribute: the attribute class is the class label. • The attributes, temp (temperature) and humidity are numerical attributes • Other attributes are categorical, that is, they cannot be ordered. • Based on the training data set, we want to find a set of rules to know what values of outlook, temperature, humidity and wind, determine whether or not to play golf.
- 10. • RULE 1 If it is sunny and the humidity is not above 75%, then play. • RULE 2 If it is sunny and the humidity is above 75%, then do not play. • RULE 3 If it is overcast, then play. • RULE 4 If it is rainy and not windy, then play. • RULE 5 If it is rainy and windy, then don't play.
- 11. Splitting attribute • At every node there is an attribute associated with the node called the splitting attribute • Top-down traversal – In our example, outlook is the splitting attribute at root. – Since for the given record, outlook = rain, we move to the rightmost child node of the root. – At this node, the splitting attribute is windy and we find that for the record we want classify, windy = true. – Hence, we move to the left child node to conclude that the class label Is "no play".
- 14. Decision tree construction • Identify the splitting attribute and splitting criterion at every level of the tree • Algorithm – Iterative Dichotomizer (ID3)
- 15. Iterative Dichotomizer (ID3) • Quinlan (1986) • Each node corresponds to a splitting attribute • Each edge is a possible value of that attribute. • At each node the splitting attribute is selected to be the most informative among the attributes not yet considered in the path from the root. • Entropy is used to measure how informative is a node.
- 17. Splitting attribute selection • The algorithm uses the criterion of information gain to determine the goodness of a split. – The attribute with the greatest information gain is taken as the splitting attribute, and the data set is split for all distinct values of the attribute values of the attribute. • Example: 2 classes: C1, C2, pick A1 or A2
- 18. Entropy – General Case • Impurity/Inhomogeneity measurement • Suppose X takes n values, V1, V2,… Vn, and P(X=V1)=p1, P(X=V2)=p2, … P(X=Vn)=pn • What is the smallest number of bits, on average, per symbol, needed to transmit the symbols drawn from distribution of X? It’s E(X) = p1 log2 p1 – p2 log2 p2 – … pnlog2pn • E(X) = the entropy of X )(log 1 2 i n i i pp∑= −=
- 21. Information gain
- 23. • Gain(S,Wind)? • Wind = {Weak, Strong} • S = {9 Yes &5 No} • Sweak = {6 Yes & 2 No | Wind=Weak} • Sstrong = {3 Yes &3 No | Wind=Strong}
- 24. Example: Decision tree learning • Choose splitting attribute for root among {Outlook, Temperature, Humidity, Wind}? – Gain(S, Outlook) = ... = 0.246 – Gain(S, Temperature) = ... = 0.029 – Gain(S, Humidity) = ... = 0.151 – Gain(S, Wind) = ... = 0.048
- 25. • Gain(Ssunny,Temperature) = 0,57 • Gain(Ssunny, Humidity) = 0,97 • Gain(Ssunny, Windy) =0,019
- 26. Over-fitting example • Consider adding noisy training example #15 – Sunny, hot, normal, strong, playTennis = No • What effect on earlier tree?
- 27. Over-fitting
- 28. Avoid over-fitting • Stop growing when data split not statistically significant • Grow full tree then post-prune • How to select best tree – Measure performance over training tree – Measure performance over separate validation dataset – MDL minimize • size(tree) + size(misclassifications(tree))
- 29. Reduced-error pruning • Split data into training and validation set • Do until further pruning is harmful – Evaluate impact on validation set of pruning each possible node – Greedily remove the one that most improves validation set accuracy
- 30. Rule post-pruning • Convert tree to equivalent set of rules • Prune each rule independently of others • Sort final rules into desired sequence for use
- 31. Issues in Decision Tree Learning • How deep to grow? • How to handle continuous attributes? • How to choose an appropriate attributes selection measure? • How to handle data with missing attributes values? • How to handle attributes with different costs? • How to improve computational efficiency? • ID3 has been extended to handle most of these. The resulting system is C4.5 (http://cis- linux1.temple.edu/~ingargio/cis587/readings/id3-c45.html)
- 32. Decision tree – When?
- 33. References • Data mining, Nhat-Quang Nguyen, HUST • http://www.cs.cmu.edu/~awm/10701/slides/ DTreesAndOverfitting-9-13-05.pdf
- 34. RANDOM FORESTS Credits: Michal Malohlava @Oxdata
- 35. Motivation • Training sample of points covering area [0,3] x [0,3] • Two possible colors of points
- 36. • The model should be able to predict a color of a new point
- 37. Decision tree
- 38. How to grow a decision tree • Split rows in a given node into two sets with respect to impurity measure – The smaller, the more skewed is distribution – Compare impurity of parent with impurity of children
- 39. When to stop growing tree • Build full tree or • Apply stopping criterion - limit on: – Tree depth, or – Minimum number of points in a leaf
- 40. How to assign leaf value? • The leaf value is – If leaf contains only one point then its color represents leaf value • Else majority color is picked, or color distribution is stored
- 41. Decision tree • Tree covered whole area by rectangles predicting a point color
- 42. Decision tree scoring • The model can predict a point color based on its coordinates.
- 43. Over-fitting • Tree perfectly represents training data (0% training error), but also learned about noise!
- 44. • And hence poorly predicts a new point!
- 45. Handle over-fitting • Pre-pruning via stopping criterion! • Post-pruning: decreases complexity of model but helps with model generalization • Randomize tree building and combine trees together
- 48. Randomize #1- Bagging • Each tree sees only sample of training data and captures only a part of the information. • Build multiple weak trees which vote together to give resulting prediction – voting is based on majority vote, or weighted average
- 49. Bagging - boundary • Bagging averages many trees, and produces smoother decision boundaries.
- 50. Randomize #2 - Feature selection Random forest
- 51. Random forest - properties • Refinement of bagged trees; quite popular • At each tree split, a random sample of m features is drawn, and only those m features are considered for splitting. Typically • m=√p or log2(p), where p is the number of features • For each tree grown on a bootstrap sample, the error rate for observations left out of the bootstrap sample is monitored. This is called the “out-of-bag” error rate. • Random forests tries to improve on bagging by “de- correlating” the trees. Each tree has the same expectation
- 52. Advantages of Random Forest • Independent trees which can be built in parallel • The model does not overfit easily • Produces reasonable accuracy • Brings more features to analyze data variable importance, proximities, missing values imputation
- 53. Out of bag points and validation • Each tree is built over a sample of training points. • Remaining points are called “out-of- bag” (OOB). These points are used for valida@on as a good approxima@on for generaliza@on error. Almost iden@cal as N-‐fold cross valida@on.