Adapting binary stacking example to multiclass
<p>I have been studying <a href="https://github.com/emanuele/kaggle_pbr/blob/master/blend.py" rel="nofollow">this example of stacking</a>. In this case, each set of K-folds produces one column of data, and this is repeated for each classifier. I.e: the matrices for blending are:</p>

<pre><code>dataset_blend_train = np.zeros((X.shape[0], len(clfs)))
dataset_blend_test = np.zeros((X_submission.shape[0], len(clfs)))
</code></pre>

<p>I need to stack predictions from a multiclass problem (probs 15 different classes per sample). This will produce an n*15 matrix for each clf.</p>

<p>Should these matrices just be concatenated horizontally? Or should they be combined in some other way, before logistic regression is applied? Thanks. </p>