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When training XGboost model I observe the following outputs:

[10]    train-rmspe:0.360292    eval-rmspe:0.843193
[11]    train-rmspe:0.358901    eval-rmspe:0.848542
[12]    train-rmspe:0.355327    eval-rmspe:0.878116
[13]    train-rmspe:0.349120    eval-rmspe:0.880048
[14]    train-rmspe:0.343729    eval-rmspe:0.886429
[15]    train-rmspe:0.337795    eval-rmspe:0.887312
[16]    train-rmspe:0.331385    eval-rmspe:0.892312
[17]    train-rmspe:0.329000    eval-rmspe:0.892327
[18]    train-rmspe:0.325391    eval-rmspe:0.892305
[19]    train-rmspe:0.323480    eval-rmspe:0.894754
[20]    train-rmspe:0.321171    eval-rmspe:0.892071
[21]    train-rmspe:0.320194    eval-rmspe:0.893531
[22]    train-rmspe:0.318526    eval-rmspe:0.892274
[23]    train-rmspe:0.315825    eval-rmspe:0.903235
[24]    train-rmspe:0.315040    eval-rmspe:0.901118
[25]    train-rmspe:0.313372    eval-rmspe:0.905540
[26]    train-rmspe:0.312313    eval-rmspe:0.905291
[27]    train-rmspe:0.311462    eval-rmspe:0.908073

I don't understand why the error on a training set is decreasing, while the error on the validation set is increasing. What is the meaning of this? It happens with all the data sub-sets...

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    $\begingroup$ You are overfitting. $\endgroup$ Nov 29, 2015 at 13:10
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    $\begingroup$ Overfitting, in other words: your model is beginning to become too much specific to the training dataset, and it now lacks generalization capability. The PAC learning framework explains why, and what you want: you want the most specific model that can fit your data, but not too much specific. $\endgroup$
    – gaborous
    Nov 29, 2015 at 17:33

2 Answers 2

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It is overfitting. Try using less features, use regularization, pick simpler model, etc. The following picture shows how training and validation sets errors depend on model complexity. I hope it helps you to develop intuition why you have overfitting problem. error - model complexity dependence

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You're overfitting. By making your model too complex, your model is finding patterns in your data that are not really there (these "patterns" are just errors/random noise). Your model is then using these false patterns to make predictions when really it should be ignoring them.

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