Candidate_Elimination_Implementation---Machine_Learning

This includes implementation of Candidate Elimination Algorithm. The training data is taken from the file provided above, named "data.txt". The source of this dataset can be found here.

The Algorithm is described below:

• Initialize G, the set of maximally general hypotheses, to contain one element: the null description (all features are variables).
• Initialize S, the set of maximally specific hypotheses, to contain one element: the first positive example.
• Accept a new training example.
  • If the example is positive:
    1. Generalize all the specific models to match the positive example, but ensure the following:
       • The new specific models involve minimal changes.
       • Each new specific model is a specialization of some general model.
       • No new specific model is a generalization of some other specific model.
    2. Prune away all the general models that fail to match the positive example.
  • If the example is negative:
    1. Specialize all general models to prevent match with the negative example, but ensure the following:
       • The new general models involve minimal changes.
       • Each new general model is a generalization of some specific model.
       • No new general model is a specialization of some other general model.
    2. Prune away all the specific models that match the negative example.
  • If S and G are both singleton sets, then:
    • if they are identical, output their value and halt.
    • if they are different, the training cases were inconsistent. Output this result and halt.
    • else continue accepting new training examples.