I'm working on a project and we are using XGBoost to make predictions. My colleague sent me the model file but when I load on my computer it don't run as expected.When I changed one variable from the model from 0 to 1 it didn't changed the result (in 200 different lines), so I started to investigate. We compared a lot of different results and it was all different.I run the xgb_tree it showed the max_depth is 0, but it is supposed to be 4. When I run xgb_tree$results it says the max_depth = 4.We also tried a lot of different save methods (.rda, .rds, .model) but none of them worked.Any suggestion would be welcome, thanks.EDIT: Posting the sessionInfo()His:R version 3.2.5 (2016-04-14)Platform: x86_64-w64-mingw32/x64 (64-bit)Running under: Windows >= 8 x64 (build 9200)locale:[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          [5] LC_TIME=English_United States.1252    attached base packages:[1] stats     graphics  grDevices utils     datasets  methods   base     other attached packages: [1] plyr_1.8.4            car_2.1-4             acepack_1.4.1         Ckmeans.1d.dp_3.4.6-4 [5] Hmisc_4.0-0           Formula_1.2-1         survival_2.40-1       memisc_0.99.7-1       [9] MASS_7.3-45           Information_0.0.9     minerva_1.4.5         randomForest_4.6-12  [13] pROC_1.8              xgboost_0.4-4         caret_6.0-73          lattice_0.20-33      [17] ggplot2_2.2.0         miscTools_0.6-22      reshape_0.8.6         data.table_1.9.8     [21] dplyr_0.5.0           e1071_1.6-7           lubridate_1.6.0       psych_1.6.9          [25] readr_1.0.0           stringr_1.1.0         stringi_1.1.2        loaded via a namespace (and not attached): [1] Rcpp_0.12.8         class_7.3-14        assertthat_0.1      digest_0.6.10       [5] foreach_1.4.3       R6_2.2.0            MatrixModels_0.4-1  stats4_3.2.5        [9] lazyeval_0.2.0      minqa_1.2.4         SparseM_1.74        nloptr_1.0.4       [13] rpart_4.1-10        Matrix_1.2-4        labeling_0.3        splines_3.2.5      [17] lme4_1.1-12         foreign_0.8-66      munsell_0.4.3       compiler_3.2.5     [21] mnormt_1.5-5        mgcv_1.8-12         htmltools_0.3.5     nnet_7.3-12        [25] tibble_1.2          gridExtra_2.2.1     htmlTable_1.7       codetools_0.2-14   [29] ModelMetrics_1.1.0  grid_3.2.5          nlme_3.1-125        gtable_0.2.0       [33] DBI_0.5-1           magrittr_1.5        scales_0.4.1        reshape2_1.4.2     [37] doParallel_1.0.10   latticeExtra_0.6-28 RColorBrewer_1.1-2  iterators_1.0.8    [41] tools_3.2.5         parallel_3.2.5      pbkrtest_0.4-6      colorspace_1.3-1   [45] cluster_2.0.3       knitr_1.15.1        quantreg_5.29Mine:R version 3.3.1 (2016-06-21)Platform: x86_64-w64-mingw32/x64 (64-bit)Running under: Windows >= 8 x64 (build 9200)locale:[1] LC_COLLATE=Portuguese_Brazil.1252  LC_CTYPE=Portuguese_Brazil.1252    LC_MONETARY=Portuguese_Brazil.1252[4] LC_NUMERIC=C                       LC_TIME=Portuguese_Brazil.1252    attached base packages:[1] stats     graphics  grDevices utils     datasets  methods   base     other attached packages:[1] caret_6.0-73         ggplot2_2.1.0        lattice_0.20-33      plyr_1.8.4           xgboost_0.4-4       [6] shinydashboard_0.5.3 shiny_0.14.2        loaded via a namespace (and not attached): [1] Rcpp_0.12.8        nloptr_1.0.4       iterators_1.0.8    tools_3.3.1        digest_0.6.10      lme4_1.1-12        [7] jsonlite_1.1       nlme_3.1-128       gtable_0.2.0       mgcv_1.8-12        Matrix_1.2-6       foreach_1.4.3     [13] parallel_3.3.1     SparseM_1.72       stringr_1.1.0      MatrixModels_0.4-1 stats4_3.3.1       grid_3.3.1        [19] nnet_7.3-12        data.table_1.9.6   R6_2.1.3           minqa_1.2.4        reshape2_1.4.2     car_2.1-3         [25] magrittr_1.5       scales_0.4.0       codetools_0.2-14   ModelMetrics_1.1.0 htmltools_0.3.5    MASS_7.3-45       [31] splines_3.3.1      rsconnect_0.4.3    pbkrtest_0.4-6     mime_0.5           xtable_1.8-2       colorspace_1.2-6  [37] httpuv_1.3.3       quantreg_5.29      stringi_1.1.1      munsell_0.4.3      chron_2.3-47      
Problem when loading a XGBoost model in a different computer
machine learning;r;xgboost
The error was due to the R version. My colleague was running a 3.2.5 and I was running the 3.3.1, and thanks to Stereo I could notice this and test on the same versions.