Add_xgboost

[PanyiDong]

No description provided.

[codecov-commenter]

Codecov Report

Merging #9 (bd4e8e5) into master (33afcd6) will increase coverage by 11.22%.
The diff coverage is 51.47%.

@@             Coverage Diff             @@
##           master       #9       +/-   ##
===========================================
+ Coverage   29.26%   40.48%   +11.22%     
===========================================
  Files          41       47        +6     
  Lines        3332     4021      +689     
===========================================
+ Hits          975     1628      +653     
- Misses       2357     2393       +36     

Flag	Coverage Δ
unittests	40.48% <51.47%> (+11.22%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
My_AutoML/_balancing/_under_sampling.py	52.39% <0.00%> (+1.65%)	⬆️
...hyperparameters/_ray/_classifier_hyperparameter.py	100.00% <ø> (ø)
..._hyperparameters/_ray/_regressor_hyperparameter.py	100.00% <ø> (ø)
My_AutoML/_imputation/_multiple.py	32.65% <ø> (+14.28%)	⬆️
My_AutoML/_model/_lightgbm.py	18.51% <18.51%> (ø)
My_AutoML/_utils/_optimize.py	51.98% <57.14%> (+48.59%)	⬆️
My_AutoML/_hpo/_ML.py	66.41% <60.00%> (+52.34%)	⬆️
My_AutoML/_model/_xgboost.py	70.17% <70.17%> (ø)
My_AutoML/_hpo/_base.py	47.56% <80.95%> (+40.86%)	⬆️
My_AutoML/_model/__init__.py	77.77% <100.00%> (ø)
... and 18 more

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[PanyiDong]

build_nn failed as neural network fail to converge. I currently have no idea on how to improve the stability, but to run the test one more time to avoid reporting infinity (the direct reason build_nn failed). The pipeline still works (as I tested), but there are possibilities the fitting will fail.