Map > Data Mining > Explaining the Past > Data Exploration > Univariate Analysis > Binning > Supervised
 

Supervised Binning

Supervised binning methods transform numerical variables into categorical counterparts and refer to the target (class) information when selecting discretization cut points. Entropy-based binning is an example of a supervised binning method.
 

Entropy-based Binning

Entropy based method uses a split approach. The entropy (or the information content) is calculated based on the class label. Intuitively, it finds the best split so that the bins are as pure as possible that is the majority of the values in a bin correspond to have the same class label. Formally, it is characterized by finding the split with the maximal information gain. 
 
Example:
Discretize the temperature variable using entropy-based binning algorithm.
 
Step 1: Calculate "Entropy" for the target.

O-Ring Failure
Y N
7 17
E (Failure) = E(7, 17) = E(0.29, .71) = -0.29 x log2(0.29) - 0.71 x log2(0.71) = 0.871

 

Step 2: Calculate "Entropy" for the target given a bin.

O-Ring Failure
Y N
Temperature  <= 60 3 0
 > 60 4 17
 E (Failure,Temperature) = P(<=60) x E(3,0) + P(>60) x E(4,17) = 3/24 x 0 + 21/24 x 0.7= 0.615

 

Step 3: Calculate "Information Gain" given a bin.

Information Gain (Failure, Temperature) = 0.256

  

The information gains for all three bins show that the best interval for "Temperature" is (<=60, >60) because it returns the highest gain.

 
Exercise