holub008 xrf issues - Githubissues

holub008 / xrf

eXtreme RuleFit (sparse linear models on XGBoost ensembles)

Other

43 stars 13 forks source link

issues

Newest

Newest Most commented Recently updated Oldest Least commented Least recently updated

In xrf workflow, prediction fails if i ask for a single value

#26 hectorsegal opened 3 months ago
0
Feature importance

#25 snvv opened 1 year ago
0
prep v0.2.2 release candidate

#24 simonpcouch closed 2 years ago
2
address incompatible types row binding error

#23 simonpcouch closed 2 years ago
6
quiet xgb warnings when prefit

#22 simonpcouch closed 2 years ago
1
quiet name repair `colnames` message

#21 simonpcouch closed 2 years ago
2
Release 0.2.1

#20 holub008 closed 2 years ago
1
Current xgboost objective is now deprecated

#19 juliasilge closed 2 years ago
4
Error: Can't combine `..1$feature` <character> and `..61$feature` <logical>

#18 ruiruimufeng closed 2 years ago
4
Representation of rules involving binary features?

#17 rjordan-hdai closed 2 years ago
5
Warning on nrounds unused

#16 holub008 closed 2 years ago
1
problem with interpreting rules

#15 Naviden closed 3 years ago
3
Error in storage.mode(y) <- "double"

#14 Naviden opened 3 years ago
3
Cut new CRAN release

#13 holub008 closed 4 years ago
5
Printing

#12 topepo closed 4 years ago
1
GitHub actions

#11 topepo closed 4 years ago
2
Fixes for #9

#10 topepo closed 4 years ago
1
Predicting from a dataframe without the response column

#9 holub008 closed 4 years ago
0
Gracefully handle NAs in predictors

#8 holub008 opened 5 years ago
0
Corrected bug in evaluate_rule_dense_only

#7 yama1968 closed 5 years ago
7
A correction for XGBoost classifying sparse one-hot 0s as "Missing"

#6 holub008 closed 5 years ago
0
Slow rule evaluation

#5 holub008 closed 5 years ago
3
Corrected following your remarks and added more flexibility on glmnet parameters

#4 yama1968 closed 5 years ago
1
Corrected problem when missing pmax

#3 yama1968 closed 5 years ago
0
Incorrect categorical split rule derivation from xgboost model built on sparse data

#2 holub008 closed 5 years ago
4
addition of pmax and weights parameters, plus parallel option on glmnet

#1 yama1968 closed 5 years ago
3