code: function ( truth, guess ) {
      // If prediction failed then return false!
      if ( guess === unknown || !labelsObj[ truth ] || !labelsObj[ guess ] ) return false;
      // Update confusion matrix.
      if ( guess === truth ) {
        cm[ truth ][ guess ] += 1;
      } else {
        cm[ guess ][ truth ] += 1;
      }
      evaluated = true;
      return true;
    }
docs: reset() 
  
  Creates an instance of cross validator useful for machine learning tasks.
  
  @param {string} truth - the actual class label.
  @param {string} guess - the predicted class label.
  @return {boolean} returns true if the evaluation is successful. The evaluation
  may fail if `truth` or `guess` is not in the array `classLabels` provided at
  instance creation time; or if guess is equal to `unknown`.