Package: gbm Version: 2.1.1 For the bernoulli distribution model, predict.gbm(model, n.trees=c(1,2), single.tree=TRUE) returns the correct results.
For the multinomial distribution model with 3 classes, the results are incorrect. The first tree is accurate, but the results for the second tree appears to contain predictions for two different classes, and third value I cannot identify. Although the data I’m using can’t be shared, the results are clearly inaccurate as they do not appear in the first 6 trees. pretty.gbm.tree(current_upsell_gbm, 1) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 0.99070843 1 5 6 79.811835 4585 -0.01284530 # 1 0 0.78340727 2 3 4 9.726234 1669 0.02624326 # 2 -1 0.06888412 -1 -1 -1 0.000000 233 0.06888412 # 3 -1 0.01932451 -1 -1 -1 0.000000 1436 0.01932451 # 4 -1 0.02624326 -1 -1 -1 0.000000 1669 0.02624326 # 5 -1 -0.03568803 -1 -1 -1 0.000000 2856 -0.03568803 # 6 -1 -0.01284530 -1 -1 -1 0.000000 60 -0.01284530 pretty.gbm.tree(current_upsell_gbm, 2) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 9.907084e-01 1 2 6 25.187460 4585 0.0001823204 # 1 -1 -2.174955e-02 -1 -1 -1 0.000000 1669 -0.0217495506 # 2 17 4.118500e+04 3 4 5 5.837505 2856 0.0129989496 # 3 -1 1.728153e-02 -1 -1 -1 0.000000 2426 0.0172815334 # 4 -1 -1.116279e-02 -1 -1 -1 0.000000 430 -0.0111627907 # 5 -1 1.299895e-02 -1 -1 -1 0.000000 2856 0.0129989496 # 6 -1 1.823204e-04 -1 -1 -1 0.000000 60 0.0001823204 pretty.gbm.tree(current_upsell_gbm, 3) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 0.968661668 1 2 6 16.796788 4585 0.012662983 # 1 -1 -0.006388166 -1 -1 -1 0.000000 1538 -0.006388166 # 2 4 3.500000000 3 4 5 7.753722 2987 0.022472380 # 3 -1 0.030072289 -1 -1 -1 0.000000 2075 0.030072289 # 4 -1 0.005180921 -1 -1 -1 0.000000 912 0.005180921 # 5 -1 0.022472380 -1 -1 -1 0.000000 2987 0.022472380 # 6 -1 0.012662983 -1 -1 -1 0.000000 60 0.012662983 pretty.gbm.tree(current_upsell_gbm, 4) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 0.96843894 1 5 6 80.145080 4585 -0.01039978 # 1 0 0.92423139 2 3 4 7.579153 1497 0.03221919 # 2 -1 0.04372024 -1 -1 -1 0.000000 977 0.04372024 # 3 -1 0.01061048 -1 -1 -1 0.000000 520 0.01061048 # 4 -1 0.03221919 -1 -1 -1 0.000000 1497 0.03221919 # 5 -1 -0.03153981 -1 -1 -1 0.000000 3018 -0.03153981 # 6 -1 -0.01039978 -1 -1 -1 0.000000 70 -0.01039978 pretty.gbm.tree(current_upsell_gbm, 5) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 9.907084e-01 1 2 6 22.723666 4585 0.0009275118 # 1 -1 -2.021911e-02 -1 -1 -1 0.000000 1644 -0.0202191098 # 2 15 5.216700e+04 3 4 5 6.990267 2871 0.0130365491 # 3 -1 1.779042e-02 -1 -1 -1 0.000000 2423 0.0177904212 # 4 -1 -1.267468e-02 -1 -1 -1 0.000000 448 -0.0126746834 # 5 -1 1.303655e-02 -1 -1 -1 0.000000 2871 0.0130365491 # 6 -1 9.275118e-04 -1 -1 -1 0.000000 70 0.0009275118 pretty.gbm.tree(current_upsell_gbm, 6) # SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction # 0 0 9.684389e-01 1 2 6 21.27357 4585 0.008641809 # 1 -1 -1.311138e-02 -1 -1 -1 0.00000 1497 -0.013111385 # 2 17 4.118500e+04 3 4 5 8.95109 3018 0.019431912 # 3 -1 1.425335e-02 -1 -1 -1 0.00000 2548 0.014253350 # 4 -1 4.750633e-02 -1 -1 -1 0.00000 470 0.047506331 # 5 -1 1.943191e-02 -1 -1 -1 0.00000 3018 0.019431912 # 6 -1 8.641809e-03 -1 -1 -1 0.00000 70 0.008641809 predict(current_upsell_gbm, off_test1[1,], n.trees=c(1,2)) # , , 1 # -1 0 1 # [1,] 0.06888412 -0.02174955 -0.006388166 # , , 2 # -1 0 1 # [1,] 0.1126044 -0.04196866 -0.01949955 0.06888412 + 0.04372024 # [1] 0.1126044 -2.174955e-02 + -2.021911e-02 # [1] -0.04196866 -0.006388166 + -1.311138e-02 # [1] -0.01949955 predict(current_upsell_gbm, off_test1[1,], n.trees=c(1,2), single.tree=TRUE) #, , 1 # -1 0 1 #[1,] 0.06888412 -0.02174955 -0.006388166 #, , 2 # -1 0 1 #[1,] -0.04196866 -0.01949955 0.07857462 I am using Ubuntu 14.04.3 LTS (GNU/Linux 3.13.0-63-generic x86_64), R version 3.2.2 (2015-08-14) -- "Fire Safety" Platform: x86_64-pc-linux-gnu (64-bit)
