Automated Feature Engineering with FeatureTools

• 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.
• I referenced Will Koehrsen's post and his code on Kaggle.

• 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.