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[R] adopt demos and vignettes to a more consistent parameter style

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This commit is contained in:
Vadim Khotilovich
2016-06-27 02:00:39 -05:00
parent a0aa305268
commit 3b6b344561
11 changed files with 59 additions and 59 deletions
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18
R-package/vignettes/discoverYourData.Rmd
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@@ -168,8 +168,8 @@ Build the model
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)).
```{r}
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,
eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 4,
eta = 1, nthread = 2, nrounds = 10,objective = "binary:logistic")
```
@@ -179,7 +179,7 @@ A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitti
> Here you can see the numbers decrease until line 7 and then increase.
>
> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nround = 4`. I will let things like that because I don't really care for the purpose of this example :-)
> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nrounds = 4`. I will let things like that because I don't really care for the purpose of this example :-)
Feature importance
------------------
@@ -189,10 +189,10 @@ Feature importance
### Build the feature importance data.table
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).
Remember, each binary column corresponds to a single value of one of *categorical* features.
```{r}
importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
head(importance)
```
@@ -215,7 +215,7 @@ 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(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sparse_matrix, label = output_vector)
importanceRaw <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst, data = sparse_matrix, label = output_vector)
# Cleaning for better display
importanceClean <- importanceRaw[,`:=`(Cover=NULL, Frequency=NULL)]
@@ -328,12 +328,12 @@ train <- agaricus.train
test <- agaricus.test
#Random Forest™ - 1000 trees
bst <- xgboost(data = train$data, label = train$label, max.depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nround = 1, objective = "binary:logistic")
bst <- xgboost(data = train$data, label = train$label, max_depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nrounds = 1, objective = "binary:logistic")
#Boosting - 3 rounds
bst <- xgboost(data = train$data, label = train$label, max.depth = 4, nround = 3, objective = "binary:logistic")
bst <- xgboost(data = train$data, label = train$label, max_depth = 4, nrounds = 3, objective = "binary:logistic")
```
> Note that the parameter `round` is set to `1`.
> [**Random Forests™**](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm) is a trademark of Leo Breiman and Adele Cutler and is licensed exclusively to Salford Systems for the commercial release of the software.
> [**Random Forests™**](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm) is a trademark of Leo Breiman and Adele Cutler and is licensed exclusively to Salford Systems for the commercial release of the software.