jupyter notebook + xgboost

今天jupyter notebook 大大学习。
xgboost 弄了一下，然后听了Kaggle GM的说法，
one must NOT use either one of the following when using XGBoost: one hot encoding, missing value fill, and input rescaling,

听着的时候，这些是没有听进去的，做了一遍后，再听，瞬间觉得重点完全变得不一样了。

就像从我现在的角度，看旅店的规划，发现旅店都集中在有大公司的地方，不是偶然，而是方便出差，而且这种出差往王阔气，多天，因为是公司负责。

明天，

1. CV with important graph for 3 stores

2. Massive feature engineering

3. LSTM try out
target encoding - https://maxhalford.github.io/blog/target-encoding-done-the-right-way/
add new feature/remove useless feature
skewness test of target variable
textual feature of catalog

Feature:
discount strength
isMajor
isHow
pages



Feature Engineering:
https://www.slideshare.net/0xdata/feature-engineering-83511751
https://www.slideshare.net/HJvanVeen/feature-engineering-72376750