update links dmlc
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@@ -17,7 +17,7 @@ Introduction
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The purpose of this Vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
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This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
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This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
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Pacakge loading:
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@@ -34,7 +34,7 @@ Preparation of the dataset
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==========================
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Numeric VS categorical variables
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----------------------------------
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--------------------------------
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**Xgboost** manages only `numeric` vectors.
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@@ -163,7 +163,7 @@ output_vector = df[,Improved] == "Marked"
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Build the model
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===============
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The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
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The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
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```{r}
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bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,
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