Decision Trees. Predic-on: Based on a bunch of IF THEN ELSE rules. Fi>ng: Find a bunch of IF THEN ELSE rules to cover all cases as best you can.
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1 Decision Trees
2 Decision Trees Predic-on: Based on a bunch of IF THEN ELSE rules Fi>ng: Find a bunch of IF THEN ELSE rules to cover all cases as best you can.
3
4
5 Each inner node is a decision based on a feature Each leaf node is a class label
6 Each inner node is a decision based on a feature Each leaf node is a class label Predic-ng Titanic Survivors
7 It can also be used for regression CART (Classifica-on and Regression Tree)
8 Each inner node is a decision based on a feature Each leaf node is a predicted value Predic-ng precipita-on in Himalayas from eleva-on, slope and posi-on
9
10 from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeRegressor
11 Advantages: The decisions are easy to understand and interpret. Both numerical and categorical features can be used naturally. Natural mul-class classifier.
12 Advantages: The decisions are easy to understand and interpret. Both numerical and categorical features can be used naturally. Natural mul-class classifier. Disadvantages: Can overfit to training data with complex trees. Small changes in input data can result in totally different trees. Can make mistakes with unbalanced classes. No confidence intervals (regression).
13 So, how do we build the best tree?
14 Like we fit any other model? Define a cost func-on: a score for a specific model (tree) Try a bunch of models (trees), choose best Use op-miza-on algorithm to try in a smart way
15 Like we fit any other model? Define a cost func-on: a score for a specific model (tree) Try a bunch of models (trees), choose best Use op-miza-on algorithm to try in a smart way
16 Build tree split by split, find the best split you can at each step
17 Build tree split by split, find the best split you can at each step
18 Build tree split by split, find the best split you can at each step
19 Build tree split by split, find the best split you can at each step
20 Use a heuris-c: Greedy search Build tree split by split, find the best split you can at each step?
21 Informa-on Entropy i H = p(x i )log p(x i )
22 Split with the max entropy rule. Repeat. Leaf node if only one class leu (or a performance metric reached, or maximum depth reached)
23 Split with the max entropy rule. Repeat. Leaf node if only one class leu (or a performance metric reached, or maximum depth reached)
24 Split with the max entropy rule. Repeat. Leaf node if only one class leu (or a performance metric reached, or maximum depth reached)
25 Split with the max entropy rule. Repeat. Leaf node if only one class leu (or a performance metric reached, or maximum depth reached)
26 May overfit. Prune.
27 May overfit. Prune.
28 May overfit. Prune.
29 BeWer yet: Use ensemble methods
30 Ensemble Methods: Bagging
31 Bootstrap aggrega-ng Problem: Overfi>ng to training set Solu-on: Bootstrap training set into mul-ple sets Fit a model to each random set Each model has one vote, choose max vote
32 Training set Test set
33 Bootstrap: Sample with replacement
34 Bootstrap: Sample with replacement
35 Classifica-on: Major vote Regression: Average outcome
36 from sklearn.ensemble import BaggingClassifier
37 Ensemble Methods: Random Forests
38 Tree Bagging with a twist
39 Introduce randomness when building each tree.
40 Introduce randomness when building each tree. For a split, do not take the best feature split.
41 Introduce randomness when building each tree. For a split, do not take the best feature split. First, randomly choose sqrt(n_feat) features.
42 Introduce randomness when building each tree. For a split, do not take the best feature split. First, randomly choose sqrt(n_feat) features. Only choose the best split among these.
43 from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor
44 from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import ExtraTreesClassifier?
45 from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import ExtraTreesClassifier Even more randomized: For each feature, split rule is random, not op-mal
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