Add xgboost model - #255
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mastoffel
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mastoffel
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Great PR! Everything but the graphviz plot (see comment) ran smoothly and made sense (though I didn't ponder over every detail).
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "graph = xgb.to_graphviz(model_basic, num_trees=1, rankdir='LR')\n", |
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this is very nice, but I had to brew install graphviz to make this work (which I think is fine, but might be worth a comment)
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just to note: I also did pip install graphviz before, but that wasn't sufficient it seems (but maybe we should add it to the requirements anyway)
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| "model_ranked, test_data, train_data = xgboost_rank_model(df_xgboost.loc[df_xgboost['points'] > 0], seed=7, test_size=0.33)\n", | ||
| "out = ranked_model_predictions(model_ranked, test_data)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "model_ranked_all, test_data_all, train_data_all = xgboost_rank_model(df_xgboost, seed=7, test_size=0.33)\n", | ||
| "out_all = ranked_model_predictions(model_ranked_all, test_data_all)" | ||
| ] | ||
| }, |
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this is a very good comparison. Interesting that accuracy drops that much when 0's are excluded (though I think we did talk about this at some point). Also, what I don't quite understand is why the accuracy in the basic XGBoost is still 20% higher than in the ranked including 0's.
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| "outputs": [], | ||
| "source": [ | ||
| "violins(out)" |
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those are super helpful to understand what's going on. It seems like there is definitely some predictive quality to the model, though not very precise (except for the top score categories maybe)
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Summary
Adds the xgboost model fitting and predictions
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Acknowledging contributors