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Cleaning in documentation

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pommedeterresautee 2015-12-02 15:48:01 +01:00
parent e384f549f4
commit 0abb4338a9
2 changed files with 3 additions and 3 deletions
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4
R-package/vignettes/discoverYourData.Rmd
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@ -190,7 +190,7 @@ Measure feature importance
In the code below, `sparse_matrix@Dimnames[[2]]` represents the column names of the sparse matrix. These names are the original values of the features (remember, each binary column == one value of one *categorical* feature).
```{r}
importance <- xgb.importance(sparse_matrix@Dimnames[[2]], model = bst)
importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
head(importance)
```
@ -213,7 +213,7 @@ One simple solution is to count the co-occurrences of a feature and a class of t
For that purpose we will execute the same function as above but using two more parameters, `data` and `label`.
```{r}
importanceRaw <- xgb.importance(sparse_matrix@Dimnames[[2]], model = bst, data = sparse_matrix, label = output_vector)
importanceRaw <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sparse_matrix, label = output_vector)
# Cleaning for better display
importanceClean <- importanceRaw[,`:=`(Cover=NULL, Frequency=NULL)]

2
R-package/vignettes/xgboostPresentation.Rmd
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@ -345,7 +345,7 @@ Feature importance is similar to R gbm package's relative influence (rel.inf).
```
importance_matrix <- xgb.importance(model = bst)
print(importance_matrix)
xgb.plot.importance(importance_matrix)
xgb.plot.importance(importance_matrix = importance_matrix)
```
View the trees from a model