parent
5c9f0ff9d9
commit
5c9f01d0a9
@ -9,7 +9,7 @@ The purpose of this Vignette is to show you how to use **Xgboost** to discover a
|
||||
|
||||
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*.
|
||||
|
||||
Pacakge loading:
|
||||
Package loading:
|
||||
|
||||
|
||||
```r
|
||||
@ -368,7 +368,7 @@ print(c2)
|
||||
## X-squared = 35.475, df = 35, p-value = 0.4458
|
||||
```
|
||||
|
||||
Pearson correlation between Age and illness disapearing is **35.48**.
|
||||
Pearson correlation between Age and illness disappearing is **35.48**.
|
||||
|
||||
|
||||
```r
|
||||
|
||||
@ -77,7 +77,7 @@ Parameters for Tree Booster
|
||||
* scale_pos_weight, [default=1]
|
||||
- Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
|
||||
* updater, [default='grow_colmaker,prune']
|
||||
- A comma separated string defining the sequence of tree updaters to run, providing a modular way to construct and to modify the trees. This is an advanced parameter that is usually set automatically, depending on some other parameters. However, it could be also set explicitely by a user. The following updater plugins exist:
|
||||
- A comma separated string defining the sequence of tree updaters to run, providing a modular way to construct and to modify the trees. This is an advanced parameter that is usually set automatically, depending on some other parameters. However, it could be also set explicitly by a user. The following updater plugins exist:
|
||||
- 'grow_colmaker': non-distributed column-based construction of trees.
|
||||
- 'distcol': distributed tree construction with column-based data splitting mode.
|
||||
- 'grow_histmaker': distributed tree construction with row-based data splitting based on global proposal of histogram counting.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user