Predicting Titanic deaths on Kaggle V: Ranger

In two previous posts (Predicting Titanic deaths on Kaggle IV: random forest revisited, Predicting Titanic deaths on Kaggle) I was unable to make random forest predict as well as boosting. Hence when I read about an alternative implementation; ranger I took the opportunity to check if with ranger I could improve predictions. The claim ranger makes is that it is faster than RandomForest.
Meanwhile, I have also been reading that RandomForest is not the best implementation. I did not bookmark where, but Google turned up Benchmarking Random Forest Classification on wise.io, and Benchmarking Random Forest Implementations on DataScience.LA.

Data

A slightly adapted version of pre-processing was used. I created a more simple version of the cabin character, thereby moving it from different variables A to F to one multilevel factor with values A, B, C, D, E, X. In addition ticket has been made in a factor with more levels.

Age

Ranger is supposed to be fast, so I took the opportunity to do a replicated cross validation for predicting missing values for age. The cross validation is replicated because in previous experiments I found the difference between two settings might be smaller than the variation within settings. For this errorest() from the ipred package was used and a wrapper around ranger’s predict was written to make it function. The plot below shows cross validation error for 10 different runs for each of the settings, so also has an indication of the variation of the cross validation error. From the plot. it was decided to chose mtry=2, nodesize=7. As can be seen from the plot, mtry 1 has the worse predictions, while at nodesize 7 and higher reasonable predictions can be made. In hindsight, bigger nodesizes might even be better, but this was not investigated at the time.

Survival

Number of trees

Conclusion

Code