Id3,c4.5 algorithim
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ID3, C4.5 :used to generate a decision tree developed by Ross Quinlan typically used in the machine learning and natural language processing domains, overview about these algorithms with illustrated examples
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Id3,c4.5 algorithim
- 1. By: Abdelfattah Al Zaqqa PSUT-AmmanJordan
- 3. Introduction-Machine Learning Machine learning is a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Machine learning and Data mining Machine Known Data learning prediction properties learned from the training data. mining discovery Previously With unknown properties in the data. overlapping Al Zaqqa-PSUT
- 4. What is Decision Tree? A decision tree is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision. Al Zaqqa-PSUT Root Node: Attribute Edges: Attribute Value Leaf Node: output, class or decision
- 5. Introduction ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset using Shannon Entropy. Typically used in the machine learning and natural language processing domains. Al Zaqqa-PSUT
- 6. ID3 basics ID3 employs Top_Down Induction of Decision Tree (greedy algorithm) Attribute selection is the fundamental step to construct a decision tree. Select which attribute will be selected to become a node of the decision tree and so on. There are two terms Entropy and Information Gain is used to process attribute selection. Al Zaqqa-PSUT
- 7. Entropy Entropy H(S) is a measure of the amount of uncertainty in the (data) set S More uniform More information we can gain More entropy More information we can gain Al Zaqqa-PSUT
- 8. Entropy set S Positive Negative Entropy(S)= - P(positive)log2P(positive) P(negative)log2P(negative) Al Zaqqa-PSUT
- 9. Information Gain is the measure of the difference in entropy from before to after the set is split on an attribute . Al Zaqqa-PSUT
- 10. Example Outlook Temperature Humidity Wind Play ball Sunny Hot High Sunny Hot High Overcast Hot High Weak Yes Mild High Weak Yes Yes Rain Weak No Strong No Rain Cool Normal Weak Rain Cool Normal Strong No Cool Normal Strong Overcast Sunny Mild High Sunny Cool Weak Yes No Sunny Overcast Yes Mild Normal Weak Yes Mild Rain Normal Weak Normal Overcast Hot Rain Yes Strong Mild High Strong Yes Normal Weak Mild High Yes Strong No Total Al Zaqqa-PSUT 14
- 11. Example-Dataset Elements Outlook Temperature Humidity Wind Play ball Sunny Hot High Sunny Hot High Overcast Hot High Weak Yes Mild High Weak Yes Yes Rain Weak No Strong No Rain Cool Normal Weak Rain Cool Normal Strong No Cool Normal Strong Overcast Sunny Mild High Sunny Cool Weak Yes No Sunny Overcast Yes Mild Normal Weak Yes Mild Rain Normal Weak Normal Overcast Hot Rain Yes Strong Mild High Strong Yes Normal Weak Mild High Yes Strong No Total Collection (S) All the records in the table refer as Collection (S). 14 Al Zaqqa-PSUT
- 12. Example-Dataset Elements Outlook Temperature Humidity Wind Play ball Sunny High Sunny Hot High Overcast Hot Attributes Hot High Weak Yes Mild High Weak Yes Yes Rain Weak No Strong No Rain Cool Normal Weak Rain Cool Normal Strong Cool Normal Strong Yes Overcast Sunny Mild High Sunny Cool No Weak No Overcast Normal Weak Yes Mild Sunny Yes Mild Rain Normal Weak Normal Mild High Overcast Hot Rain Strong Yes Strong Yes Normal Weak Strong No Total Class(C) or Classifier:Play ball Mild High Yes 14 Because based on Outlook, Temperature, Humidity and Wind we need to decide whether we can Play ball or not, that’s why Play ball is a classifier to make decision. Al Zaqqa-PSUT
- 13. ID3 Algorithm 1. Compute Entropy(S) = -(9/14)log2(9/14)-(5/14)log2(5/14)=0.940 2. Compute information gain for each attribute: Gain(S,Windy) = Entropy(S)(8/14)Entropy(Sfalse) -(6/14)Entropy(Strue) =0.048 Windy: Weak=8(6+,2-), Strong=6(3+,3-) • Entropy(Sfalse)=-6/8Log2(6/8)-2/8Log2(2/8)=0.811 • Entropy(Strue) =-3/6Log2(3/6)-3/6Log2(3/6)=1 Gain(S,Windy) = 0.940-(8/14)(0.811)-(6/14)(1)=0.048 Al Zaqqa-PSUT
- 14. ID3 Algorithm 3. Select attribute with the maximum information gain for splitting: Gain(S, Windy)=0.048 Gain(S, Humidity) =0.151 Gain(S, Temperature)=0.029 Gain(S, Outlook) = 0.246 Al Zaqqa-PSUT
- 15. ID3 Algorithm 4. Apply ID3 to each child node of this root, until leaf node or node that has entropy=0 are reached. Al Zaqqa-PSUT
- 16. C4.5 C4.5 is an extension of Quinlan's earlier ID3 algorithm. Handling both continuous and discrete attributes. Handling training data with missing attribute values Pruning trees after creation. Al Zaqqa-PSUT
- 17. Continuous-valued attributes Outlook Temperature Humidity Wind Play ball Sunny Hot 0.9 Sunny Hot 0.87 Overcast Hot 0.93 Weak Yes 0.89 Weak Yes Weak Yes Rain Mild Weak No Strong No Rain Cool 0.80 Rain Cool 0.59 Strong No Cool 0.77 Strong Overcast Sunny Mild Yes Weak 0.68 Weak Yes Mild 0.84 Weak Yes Mild Sunny 0.91 0.72 Strong Yes Mild 0.49 Strong Yes Cool Rain Sunny Overcast Overcast Hot Rain 0.74 Mild No Weak 0.86 Yes Strong No Total Al Zaqqa-PSUT 14
- 18. Continuous-valued attributes Humidity Play ball 0.9 No 0.87 1. sort the numeric attribute values, 2. Identify adjacent examples that differ in their target classification to pick the threshold. No 0.93 Yes 0.89 Yes 0.80 Yes 0.59 0.68 0.72 0.87 0.9 0.91 Humidity yes yes no no no No 0.77 0.91 Humidity Yes No 0.68 Yes 0.84 Yes 0.72 Yes 0.49 Yes 0.74 Humidity>(0.72+0.87)/2 Humidity>0.795 Yes 0.86 No Al Zaqqa-PSUT
- 20. Overfitting “Under fitting” “Just right” “Over fitting” Overfitting: If we have too many attributes(features) the learned hypothesis may fit the training set very well, but fail to generalize to new examples (Predict price on new examples). Al Zaqqa-PSUT
- 23. Why overfitting happens? Presence of error in the training examples. (In general in machine learning). When small numbers of examples are associated with leaf node. Al Zaqqa-PSUT
- 24. Reduce Overfitting Stop growing the tree earlier, before it reaches the point where it perfectly classifies the training data. (difficult) Allow the tree to overfit the data, and then post-prune the tree. Al Zaqqa-PSUT
- 25. Rule post-pruning (Outlook = Sunny " Humidity = Normal) P (Outlook = Sunny " Humidity = High) N (Outlook = Overcast) P (Outlook = Rain " Wind = Strong) N (Outlook = Rain " Wind = Weak) P Al Zaqqa-PSUT
- 26. Rule post-pruning •Prune preconditions Outlook Temp Humidity Wind Tennis Rain Low High Weak No Rain Hot High Strong No (Outlook = Sunny " Humidity = High) N (Outlook = Sunny " Humidity = Normal) P (Outlook = Overcast) P (Outlook = Rain " Wind = Strong) N (Outlook = Rain " Wind = Weak) P Al Zaqqa-PSUT
- 27. Rule post-pruning •Prune preconditions Outlook Temp Humidity Wind Tennis Rain Low High Weak No Rain Hot High Strong No (Outlook = Sunny " Humidity = High) N (Outlook = Sunny " Humidity = Normal) P (Outlook = Overcast) P (Outlook = Rain) N (Outlook = Rain " Wind = Weak) P New instances Outlook Humidity Wind Tennis Sunny Low Low Weak yes Rain Al Zaqqa-PSUT Temp Hot High Weak No
- 28. Rule post-pruning Validation set Save a portion of the data for validation Training set s Validation set Test set <= t, prune subtree {s validation performance with subtree at node, t validation set performance with leaf instead of subtree) Rule post-pruning (Quinlan 1993) Can remove smaller elements than whole subtrees Improved readability Reduced-error pruning (Quinlan 1987) … Al Zaqqa-PSUT
- 29. Missing information Example: Missing information in mammograph data BI-RAD Age shape Margin Density Class 4 48 4 5 ? 1 5 67 3 5 3 1 5 57 4 4 3 1 5 60 ? 5 1 1 4 53 ? 4 3 1 4 28 1 1 3 0 4 70 ? 2 3 0 2 66 1 1 ? 0 5 63 3 ? 3 0 4 78 1 1 1 0 Al Zaqqa-PSUT
- 30. Missing information-according to most common Fill in the data according to most common (given class) BI-RAD Age shape Margin Density Class 4 48 4 5 3 1 5 67 3 5 3 1 5 57 4 4 3 1 5 60 4 5 1 1 4 53 4 4 3 1 4 28 1 1 3 0 4 70 1 2 3 0 2 66 1 1 3 0 5 63 3 ? 3 0 4 78 1 1 0 Al Zaqqa-PSUT 1
- 32. Summery ID3, C4.5 :used to generate a decision tree developed by Ross Quinlan typically used in the machine learning and natural language processing domains ID3, C4.5: uses the entropy of an attribute and picks the attribute with the highest reduction in entropy to determine which attribute should the data be split with first and then through a series of recursive functions that calculate the entropy of the node the process is continued until all the left nodes are pure. Al Zaqqa-PSUT
Notes de l'éditeur
- to minimize the decisiontree depth, when we traverse the tree path, weneed to select the optimal attribute for splitting thetree node, which we can easily imply that theattribute with the most entropy reduction is thebest choice.