rdkit.ML.InfoTheory.entropy module¶
Informational Entropy functions
The definitions used are the same as those in Tom Mitchell’s book “Machine Learning”
- rdkit.ML.InfoTheory.entropy.PyInfoEntropy(results)¶
Calculates the informational entropy of a set of results.
Arguments
results is a 1D Numeric array containing the number of times a given set hits each possible result. For example, if a function has 3 possible results, and the
variable in question hits them 5, 6 and 1 times each, results would be [5,6,1]
Returns
the informational entropy
- rdkit.ML.InfoTheory.entropy.PyInfoGain(varMat)¶
calculates the information gain for a variable
Arguments
- varMat is a Numeric array with the number of possible occurrences
of each result for reach possible value of the given variable.
- So, for a variable which adopts 4 possible values and a result which
has 3 possible values, varMat would be 4x3
Returns
The expected information gain