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      "body": "Hi, these are some good remarks, thank you. Let me address them one by one:\n\n*\"Have you removed common words like he, she, the, a ...?\" In your found topics it seems they are still in.*\nYes, but rather arbitrarily. I just filtered any word that appears in more than one third of the training set. Apparently this has left `she` and `he` in there, but at least removed ´a` and `the`. I have to try to lower the threshold, maybe to 10 or 20% of all documents. Definitely worth  trying to find a sweet spot via cross validation.\n\n*Also html tags like \"href\" are in. Removing them could already improve your features.*\nYes, damn, I wrote a bunch of regular expression filters, I missed `href`, though. Will be included in the next version.\n\n*Have you done a e.g. 5 fold cross validation of the training and test set? This often gives a more realistic view of the predicted results.*\nI haven't done any cross validation, yet. But will definitely do to tune some hyper-parameters such as number of topics or the word filter threshold. I have to see if it makes sense to also tune some forest parameters like `max_depth`, `max_leaf_nodes`, or percentage of features at each split. What I have done though is to run the model a couple of times with a different RNG seed to see if results are consistent and robust (they are).\n\n*Did you have a look at the far outliers in the prediction and reviewed them manually? I think this could give interesting insights to improve your features.*\nI haven't done a very thorough investigation, yet. However, the truffles you are seeing in the post above are, by definition, some outliers, they have the highest difference between real payout and predicted.\n\nThanks for the feedback, really appreciated!",
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