The goal is to do automated feature engineering with FeatureTools.
A dataset of Kaggle Competition, Home Credit Default Risk was downloaded for testing. The dataset consists of 4 tables, and the relationship diagram is as follows. Various derived variables could be created automatically using FeatureTools.
A dataset with 2,221 features for 356,255 customers was finally created. Saved as a CSV file, it is about 4GB.
The whole process took 3 hours and a half on my iMac with 6 cores and 16GB of memory.
Automated Modeling with AutoGluon
The goal is to do automated modeling with AutoGluon.
AutoGluon makes it easy to automatically experiment with a variety of algorithms, from tree ensembles to deep learning and even model stacking.
model
score_val
pred_time_val
fit_time
weighted_ensemble_k0_l2
0.787430
3098.737486
95757.520068
weighted_ensemble_k0_l1
0.786499
601.329862
46704.633752
CatboostClassifier_STACKER_l1
0.786261
2511.553999
53790.340020
LightGBMClassifierXT_STACKER_l1
0.785994
2511.152501
53834.121477
LightGBMClassifier_STACKER_l1
0.785990
2511.691034
53782.292310
LightGBMClassifierCustom_STACKER_l1
0.785596
2510.629085
54090.092252
LightGBMClassifierCustom_STACKER_l0
0.782958
10.546562
1941.627757
CatboostClassifier_STACKER_l0
0.782336
7.888541
1890.238214
LightGBMClassifierXT_STACKER_l0
0.780601
11.507542
860.345474
LightGBMClassifier_STACKER_l0
0.780356
10.297791
824.519218
...
The model stacking technique achieved the highest predictive performance. This was 0.78149 for the Kaggle public board and 0.78391 for the private board as measured by AUROC.
This process took about 1 day and 6 hours to train on an AWS m4.16xlarge EC2 instance with 64 cores and 256GB of memory, and about an hour and a half to infer.