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Learning Decision Tree in Machine Learning

Principled Criterion
Selection of an attribute to test at each node- choosing the most useful attribute for classifying examples.

Information gain
  - measures how well a given attribute separates the training examples according to their target classification.
  - This measure is used to select among the candidate attributes at each step while growing the tree.
  - Gain is measure of how we can reduce uncertainty (Value lies between 0,1).

Entropy :-

A measure for -
 - uncertainty
 - purity
 - information content

Information theory: optimal length code assigns (-log2p) bits to message having probability p
S is a sample of training examples
  - p+ is the proportion of positive examples in S
  - P_ is the proportion of negative examples in S
Entropy of S : average optimal number of bits to encode information about certainty/uncertainty about S



S is a sample of training examples
p+ is the proportion of positive of positive example
p_ is the proportion of negative examples
Entropy measures the impurity of S
   Entropy(S) = -p+log2p+ - p_ log2p_
The entropy is 0 if the outcome is "certain".
The entropy is maximum if we have no knowledge of the system (or any outcome is equally possible).


Information Gain

Gain (S,A): expected reduction in entropy due to partitioning S on attribute A.

Entropy ([21+,5-]) = o.17
Entropy ([8+,30-]) = 0.74
Gain (S,A1) = Entropy(S) 
         -26/64*Entropy([21+,5-]) 
         -38/64*Entropy([8+,30-])
    = 0.27 

Entropy ([18+,33-]) = o.94
Entropy ([8+,30-]) = 0.62
Gain (S,A2) = Entropy(S) 
         -51/64*Entropy([18+,33-]) 
         -13/64*Entropy([11+,2-])
    = 0.12

Training Example :-

Selecting the next Attributes


The information gain values for the 4 attributes are:
 Gain(S,Outlook) = 0.247
 Gain(S, Humidity) = 0.151
 Gain(S,Wind) = 0.048
 Gain(S,Temperature) = 0.029

where S denotes the collection of training examples

Splitting Rule: GINI Index

GINI Index
 - Measure of node impurity

Splitting Based on Continuous Attributes

Continuous Attribute - Binary Split
   For continuous attribute
       - Partition the continuous value of attribute A into a discrete set of intervals
       - Create a new Boolean attribute Ac , looking for a threshold c,
       - Consider all possible splits and finds the best cut.


  

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