def fscore(beta)
      b2 = Float(beta**2)
      ((b2 + 1) * precision * recall) / (b2 * precision + recall)
    end
def fscore(beta)
      b2 = Float(beta**2)
      ((b2 + 1) * precision * recall) / (b2 * precision + recall)
    end
Returns the F-measure of the classifier's precision and recall.

 @param [Float] beta Favor precision (<1), recall (>1), or both (1)
 @return [Float]