Control: reassign -1 r-cran-gbm 2.1-1 On Ma, 17 nov 15, 09:29:12, David Paulsen wrote: > 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) > > > >
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