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Updates from 1.2.0 cran submission (#6077)

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* update for 1.2.0 cran submission

* recover cmakelists

* fix unittest from the shap PR

* trigger CI
This commit is contained in:
Tong He
2020-09-02 20:50:23 +08:00
committed by GitHub
parent 9be969cc7a
commit 3912f3de06
23 changed files with 203 additions and 38 deletions
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12
R-package/vignettes/discoverYourData.Rmd
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@@ -57,7 +57,7 @@ To answer the question above we will convert *categorical* variables to `numeric
In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features.
The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot).
The method we are going to see is usually called [one-hot encoding](https://en.wikipedia.org/wiki/One-hot).
The first step is to load `Arthritis` dataset in memory and wrap it with `data.table` package.
@@ -66,7 +66,7 @@ data(Arthritis)
df <- data.table(Arthritis, keep.rownames = FALSE)
```
> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](https://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
The first thing we want to do is to have a look to the first few lines of the `data.table`:
@@ -137,8 +137,8 @@ levels(df[,Treatment])
#### Encoding categorical features
Next step, we will transform the categorical data to dummy variables.
Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
@@ -176,7 +176,7 @@ bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 4,
You can see some `train-error: 0.XXXXX` lines followed by a number. It decreases. Each line shows how well the model explains your data. Lower is better.
A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
A model which fits too well may [overfit](https://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
> Here you can see the numbers decrease until line 7 and then increase.
>
@@ -304,7 +304,7 @@ Linear model may not be that smart in this scenario.
Special Note: What about Random Forests™?
-----------------------------------------
As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
As you may know, [Random Forests™](https://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) family.
Both trains several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).

2
R-package/vignettes/xgboost.bib
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@@ -24,7 +24,7 @@
author = "K. Bache and M. Lichman",
year = "2013",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
url = "http://archive.ics.uci.edu/ml/",
institution = "University of California, Irvine, School of Information and Computer Sciences"
}

2
R-package/vignettes/xgboostPresentation.Rmd
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@@ -68,7 +68,7 @@ The version 0.4-2 is on CRAN, and you can install it by:
install.packages("xgboost")
```
Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost)
Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost/)
## Learning