PaulVanDev
commented
6 years ago .train(data_d1[feature_columns],model_names=['XGBClassifier','RandomForestClassifier','GradientBoostingClassifier'])
in this case XGBoost is selected with a score of 93.2 which is better then XGBoost alone and less than GradientBoosting alone
and with compare_all_models=True, Randomforest is selected
doesn't look very consistent ;)
Hi, I tested a bit further
I tried to optimize the hyperparameters using "optimize_final_model" on XGBClassifier, and it was blocked after a while saying "saving file at 'mynotebook' " Before I saw errors like these
AttributeError: Can't get attribute 'Individual' on <module 'deap.creator' from 'C:\Users\pv\Anaconda3\lib\site-packages\deap\creator.py'>
What's wrong?
I used my data for some predictions MODEL | accuracy | training time
auto_ml - GradientBoosting | 95,50% | | 40min
2 . Gradient Boosting | 93,20% | | 15min
auto_ml - XGBoost | 92,90% | | 9sec
Results are puzzling me :)
normaly XGBoosting with 8 cores is about between 10x 20x faster than gradient boosting, here it's at least 100x... Why? why XGBoost is not so good than GradientBoosting? is there a way to get the (default) parameters from a trained model for observations?
Paul