Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques by Prabhanjan Narayanachar Tattar

Author:Prabhanjan Narayanachar Tattar [Tattar, Prabhanjan Narayanachar]
Language: eng
Format: epub, pdf
Publisher: Packt Publishing
Published: 2018-07-26T23:00:00+00:00

The bold and large font of the iteration number is not the format thrown out by the R software. This change has been made to emphasize the fact that the number of iterations from the earlier fitted spam_xgb object will now continue from 101 and will go up to 110. Adding additional iterations is easily achieved with the xgboost function.

The xgb.plot.importance function, working with the xgb.importance function, can be used to extract and display the most important variables as identified by the boosting method:

> # Variable Importance > xgb.plot.importance(xgb.importance(names(spam_train$data), + spam_xgb)[1:10,])

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