the name of importance measure to plot. When NULL, 'Gain' would be used for trees and 'Weight' would be used for gblinear. The xgb.plot.importance function creates a barplot (when plot=TRUE) and silently returns a processed data.table with n_top features sorted by importance.
  • CNevd from autohome.com ad platform team: "Distributed XGBoost is used for click through rate prediction in our display advertising, XGBoost is highly efficient and flexible and can be easily used on our distributed platform, our ctr made a great improvement with hundred millions samples and millions features due to this awesome XGBoost ...

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    Creating a “shadow” feature for each feature on our dataset, with the same feature values but only shuffled between the rows Run in a loop, until one of the stopping conditions: 2.1. We are not removing any more features 2.2. We removed enough features — we can say we want to remove 60% of our features 2.3.

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    If ‘auto’ and data is pandas DataFrame, data columns names are used. categorical_feature (list of strings or int, or 'auto', optional (default="auto")) – Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify feature_name as well). If ‘auto’ and data is pandas ...

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    つまりなにしたの? 前回XGBoostを使ってクラス分類ができることを確認した。今度は、アヤメのがく弁の長さをそれ以外の要素から予測する回帰問題として扱ってみる。

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