程序代写代做代考 database decision tree algorithm Machine Learning

Machine Learning

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Machine Learning: Lecture 3
Decision Tree Learning
(Based on Chapter 3 of Mitchell T.., Machine Learning, 1997)

thanks to Brian Pardo (http://bryanpardo.com) for the illustrations on slides 9, 18

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Decision Tree Representation
Outlook
Humidity Wind
Sunny
Overcast
Rain
High
Normal
Strong
Weak

A Decision Tree for the concept PlayTennis

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Appropriate Problems for Decision Tree Learning
Instances are represented by discrete attribute-value pairs (though the basic algorithm was extended to real-valued attributes as well)
The target function has discrete output values (can have more than two possible output values –> classes)
Disjunctive hypothesis descriptions may be required
The training data may contain errors
The training data may contain missing attribute values

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ID3: The Basic Decision Tree Learning Algorithm
See database on the next slide
What is the “best”
attribute?

Answer: Outlook
[“best” = with highest
information gain]

D13

D12

D11

D10

D14

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Day Outlook Temperature Humidity Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak Yes
D6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14 Rain Mild High Strong No

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ID3 (Cont’d)
Outlook

Sunny
Overcast
Rain
What are the
“best” attributes?
Humidity and Wind

D13

D12

D11

D10

D14

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What Attribute to choose to “best” split a node?
Choose the attribute that minimizes the Disorder (or Entropy) in the subtree rooted at a given node.
Disorder and Information are related as follows: the more disorderly a set, the more information is required to correctly guess an element of that set.
Information: What is the best strategy for guessing a number from a finite set of possible numbers? i.e., how many questions do you need to ask in order to know the answer (we are looking for the minimal number of questions). Answer: log2 |S|, where S is the set of numbers and |S|, its cardinality.

E.g.: 0 1 2 3 4 5 6 7 8 9 10
Q1: is it smaller than 5?
Q2: is it smaller than 2?
Q1
Q2

Entropy (1)
The entropy of a set is a measure for characterizing the degree of disorder or impurity in a collection of examples.
The idea was borrowed from the field of thermodynamics and relates to the states (Gas/Liquid/Solid) of a system.
For classification, we use the following formula: Entropy(S) = -((p+) * log2 (p+)) – ((p-) * log2 (p-))

Where p+ / p- represent the proportions of positive / negative examples, in S
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Entropy (2)
The entropy can also be thought of as the minimum number of bits of information necessary to encode the classification of an arbitrary member of S.
Examples:

If p+ is 1  All the examples are positive, i.e., no information is needed, Entropy(S)=0
If p+ is ½  1 bit is required to indicate whether the example is positive or negative, Entropy(S) = 1
If p+ is .8  the class can be encoded by (on average) less than 1 bit by giving short codes to positive examples and large codes to negative ones
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Entropy (3)
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Entropy (4)
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Information Gain (1)
The information gain is a measure of the effectiveness of an attribute in classifying the training data.
The information gain is also the expected reduction in entropy caused by partitioning the examples according to this attribute.
Gain(S, A), is the expected reduction in entropy caused by knowing the value of Attribute A.

Information Gain (2)
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value v.

Example: PlayTennis

Calculate the Information Gain of attribute Wind

Step 1: Calculate Entropy(S)
Step 2: Calculate Gain(S, Wind)
Calculate the Information Gain of all the other attributes (outlook, temperature and humidity)
Choose the attribute with the highest information gain.

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Hypothesis Space Search in Decision Tree Learning
Hypothesis Space: Set of possible decision trees (i.e., complete space of finite discrete-valued functions).
Search Method: Simple-to-Complex Hill-Climbing Search (only a single current hypothesis is maintained ( from candidate-elimination method)). No Backtracking!!!
Evaluation Function: Information Gain Measure
Batch Learning: ID3 uses all training examples at each step to make statistically-based decisions ( from candidate-elimination method which makes decisions incrementally). ==> the search is less sensitive to errors in individual training examples.

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Inductive Bias in Decision Tree Learning
ID3’s Inductive Bias: Shorter trees are preferred over longer trees. Trees that place high information gain attributes close to the root are preferred over those that do not.
Note: this type of bias is different from the type of bias used by Candidate-Elimination: the inductive bias of ID3 follows from its search strategy (preference or search bias) whereas the inductive bias of the Candidate-Elimination algorithm follows from the definition of its hypothesis space (restriction or language bias).

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Why Prefer Short Hypotheses?
Occam’s razor: Prefer the simplest hypothesis that fits the data [William of Occam (Philosopher), circa 1320]
Scientists seem to do that: E.g., Physicist seem to prefer simple explanations for the motion of planets, over more complex ones
Argument: Since there are fewer short hypotheses than long ones, it is less likely that one will find a short hypothesis that coincidentally fits the training data.
Problem with this argument: it can be made about many other constraints. Why is the “short description” constraint more relevant than others?
Nevertheless: Occam’s razor was shown experimentally to be a successful strategy!

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Issues in Decision Tree Learning: I. Overfitting
Definition: Given a hypothesis space H, a hypothesis hH is said to overfit the training data if there exists some alternative hypothesis h’H, such that h has smaller error than h’ over the training examples, but h’ has a smaller error than h over the entire distribution of instances.

Avoiding Overfitting the Data (1)
There are two approaches for overfitting avoidance in Decision Trees:

Stop growing the tree before it perfectly fits the data
Allow the tree to overfit the data, and then post-prune it.

Avoiding Overfitting the Data (2)
There are three criterion that can be used to determine the optimal final tree size.

Train and Validation Set Approach: use a separate set of examples (distinct from the training examples) to evaluate the utility  Reduced Error Pruning
Use all the available training data but apply a statistical test to estimate the effect of expanding or pruning a particular node.
Use an explicit measure of complexity for encoding the training examples and the decision tree.
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Avoiding Overfitting the data (3)
Reduced Error Prunning
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No

Consider each of the decision nodes in the tree
For each node, compare the performance of the tree with that

node expanded or not. Example:
3. If Right Tree is as accurate as Left Tree over the validation set
then prune that node.

Avoiding Overfitting the data (4)
Rule Post-Pruning
This is the strategy used in C4. 5:
Grow a Tree
Convert the tree into sets of rules
Prune (generalize) each rule by removing any preconditions that result in improving its estimated accuracy.
Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying subsequent instances.

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Issues in Decision Tree Learning: II. Other Issues
Incorporating Continuous-Valued Attributes
Alternative Measures for Selecting Attributes
Handling Training Examples with Missing Attribute Values
Handling Attributes with Differing Costs

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