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I am trying to interpret this chart.

I am not sure how to interpret this, because, I think that the fact of the for examples LGBM Validation error, is wide and similar to train boxplot, there arent problem of overfitting, but when I see another type of charts of the execution of LGBM, I can see that really the LGBM is overfitted, so really I don't know how to interpret this of the correct way.

enter image description here

But I don't know how could interpret beyond this:

LightGBM is maybe the best option because it is faster and finally you can get enough accuracy with that, and in comparison with the other two, bagging have less overfit because of the differences between the error is less.

Any idea?

Thanks

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    $\begingroup$ What do you not understand? If you created the chart, you must have had a reason. Can you elaborate on what confuses you? $\endgroup$
    – Valentin Calomme
    Commented Jun 27, 2020 at 17:09
  • $\begingroup$ I want to know if is possible, analice the overfit and unverfit, with this type of chart $\endgroup$
    – Tlaloc-ES
    Commented Jun 27, 2020 at 17:13
  • $\begingroup$ Is this homework or something like that? $\endgroup$
    – noe
    Commented Jun 27, 2020 at 17:17
  • $\begingroup$ Where does this diagram from? And the one from another of your questions that also came with no explanation at all? $\endgroup$
    – noe
    Commented Jun 27, 2020 at 17:18
  • $\begingroup$ Yes, this is homework, the problem was getting conclusions about the plot, but I think that my own interpretation of the plot is not enough, my interpretation is the following: LightGBM is maybe the best option because is faster and finally you can get enough accuracy with that, and in comparison with the another two, bagging have less overfit because the differences between the error are less. $\endgroup$
    – Tlaloc-ES
    Commented Jun 27, 2020 at 17:27

1 Answer 1

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Your chart seems to show that light GBM models are very inconsistent in terms of F1 score. The other two types of model tend to have lower validation accuracy than training accuracy, suggesting overfitting is occurring to some extent (but this is ubiquitous in machine learning so it’s not a deal breaker by any means). The best median validation performance is by RandomForest, however some outliers underperformed the models using bagging. Possibly a good approach would be to have an ensemble of RandomForest models.

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