How do I merge or combine error rates?
<p>Let's say I have a dataset that has 9 continuous columns of data and 4 columns of categorical data.  In Matlab, I separate the columns into two groups and do training/testing (naïve bayes) on them separately and determine that the continuous columns have an error rate of 0.45 and the categorical columns have an error  0.33.  My question is - how do I determine the combined error?</p>

<p>EDIT - Simple pseudocode overview added:</p>

<pre><code>for x = 1:num_iterations
  Mdl_NB1 = fitcnb(TrainingSet_Con,TrainingTargets,'Distribution','normal');
  Mdl_NB2 = fitcnb(TrainingSet_Dis,TrainingTargets,'Distribution','mn');
  [NB1_label,NB1_Posterior,NB1_Cost] = predict(Mdl_NB1,TestPoint_Con);
  [NB2_label,NB2_Posterior,NB2_Cost] = predict(Mdl_NB2,TestPoint_Dis);
  NB1_cumulLoss = NB1_cumulLoss + resubLoss(Mdl_NB1);
  NB2_cumulLoss = NB2_cumulLoss + resubLoss(Mdl_NB2);
end
NB1_avg_score = NB1_cumulLoss/num_iterations
NB2_avg_score = NB2_cumulLoss/num_iterations
total_avg_score = ???
</code></pre>

<p>The three obvious choices, in principle, are:</p>

<ul>
<li>(A+B) / 2</li>
<li>A * B</li>
<li>(A*(CountA/TotalCount)) + (B*(CountB/TotalCount))</li>
</ul>

<p>But not sure if any of these are right, in this case.</p>