human readable rules from xgboost in R

问题

I try to use xgboost in R to get rules (gbtree) from my data, so I can use the rules in an other system (not predicted data with 'predict'). The Input-Data have appr. 1500 colums and 40 Mio rows with binary, sparse data and the Label is a binary column, too.

library(xgboost)
library(Matrix)

labels <- data.frame(labels = sample.int(2, m*1, TRUE)-1L)
observations <- Matrix(as.matrix(data.frame(feat_01=sample.int(2, size=100, T) -1,
                                            feat_02=sample.int(2, size=100, T) -1,
                                            feat_03=sample.int(2, size=100, T) -1,
                                            feat_04=sample.int(2, size=100, T) -1,
                                            feat_05=sample.int(2, size=100, T) -1)), sparse=T)

dtrain <- xgb.DMatrix(data = observations, label = labels$labels)

bstResult <- xgb.train(data = dtrain, 
                       nthread = 1, 
                       nround = 4, 
                       max_depth = 3, 
                       verbose = T,
                       objective = "binary:logistic",
                       booster='gbtree')

xgb.dump(bstResult)
xgb.plot.tree(model = bstResult, n_first_tree = 2)

I visualize the data as xgb.dump or xgb.plot.tree. But I need the data in a form like:

rule1: feat_01 == 1 & feat_02==1 & feat_03== 0 --> Label = 1

rule2: feat_01== 0 & feat_03==1 & feat_04== 1 --> Label = 0

Is this possible or am I on the wrong track?

Regards Heiko

edit: added example and tried to make the question better

回答1:

On one hand, I think you can use the importance matrix to obtain the coverage and ranking of each feature. On the other hand, xgboost uses an ensemble of weak learners using bagging, the rules should be 'rare'

来源：https://stackoverflow.com/questions/42698231/human-readable-rules-from-xgboost-in-r

标签

xgboost