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Update xgboostPresentation.Rmd

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Tong He 2015-03-01 18:30:49 -08:00
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R-package/vignettes/xgboostPresentation.Rmd
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@ -141,7 +141,7 @@ We will train decision tree model using the following parameters:
* `objective = "binary:logistic"`: we will train a binary classification model ; * `objective = "binary:logistic"`: we will train a binary classification model ;
* `max.deph = 2`: the trees won't be deep, because our case is very simple ; * `max.deph = 2`: the trees won't be deep, because our case is very simple ;
* `nround = 2`: there will be two pass on the data, the second one will focus on the data not correctly learned by the first pass. * `nround = 2`: there will be two pass on the data, the second one will enhance the model by reducing the difference between ground truth and prediction.
```{r trainingSparse, message=F, warning=F} ```{r trainingSparse, message=F, warning=F}
bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic") bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
@ -398,7 +398,7 @@ pred3 <- predict(bst3, test$data)
print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred)))) print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
``` ```
> Again `0`? It seems that `Xgboost` works prety well! > Again `0`? It seems that `Xgboost` works pretty well!
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