Merge branch 'master' into update-gh-actions

Author: juliasilge

R/boost_tree.R

@@ -301,7 +301,7 @@ xgb_train <- function(
 
 
   if (is.numeric(y)) {
-    loss <- "reg:linear"
+    loss <- "reg:squarederror"
   } else {
     lvl <- levels(y)
     y <- as.numeric(y) - 1
@@ -399,7 +399,7 @@ xgb_pred <- function(object, newdata, ...) {
 
   x = switch(
     object$params$objective,
-    "reg:linear" = , "reg:logistic" = , "binary:logistic" = res,
+    "reg:squarederror" = , "reg:logistic" = , "binary:logistic" = res,
     "binary:logitraw" = stats::binomial()$linkinv(res),
     "multi:softprob" = matrix(res, ncol = object$params$num_class, byrow = TRUE),
     res

R/predict.R

@@ -23,9 +23,9 @@
 #'            is the parameter for the tail area of the intervals
 #'            (e.g. confidence level for confidence intervals).
 #'            Default value is 0.95.
-#'     \item `std_error`: add the standard error of fit or
-#'            prediction for `type`s of "conf_int" and "pred_int".
-#'            Default value is `FALSE`.
+#'     \item `std_error`: add the standard error of fit or prediction (on
+#'            the scale of the linear predictors) for `type`s of "conf_int"
+#'            and "pred_int". Default value is `FALSE`.
 #'     \item `quantile`: the quantile(s) for quantile regression
 #'            (not implemented yet)
 #'     \item `time`: the time(s) for hazard probability estimates

README.Rmd

@@ -21,9 +21,6 @@ knitr::opts_chunk$set(
 [![CRAN status](https://www.r-pkg.org/badges/version/parsnip)](https://CRAN.R-project.org/package=parsnip)
 [![Downloads](http://cranlogs.r-pkg.org/badges/parsnip)](https://cran.rstudio.com/package=parsnip)
 [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
-
-[![Codecov test coverage](https://codecov.io/gh/tidymodels/parsnip/branch/master/graph/badge.svg)](https://codecov.io/gh/tidymodels/parsnip?branch=master)
-[![R build status](https://github.com/tidymodels/parsnip/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/parsnip/actions)
 <!-- badges: end -->
 
 ## Introduction
@@ -129,6 +126,12 @@ Data sets previously found in `parsnip` are now find in the `modeldata` package.
 
 ## Contributing
 
-If you encounter a bug, please file a minimal reproducible example on [GitHub](https://github.com/tidymodels/parsnip/issues). For questions and other discussion, please use [community.rstudio.com](https://community.rstudio.com/).
+This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
+
+- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://rstd.io/tidymodels-community).
+
+- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/parsnip/issues).
+
+- Either way, learn how to create and share a [reprex](https://rstd.io/reprex) (a minimal, reproducible example), to clearly communicate about your code.
 
-Please note that the parsnip project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
+- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).

README.md

@@ -13,7 +13,6 @@ status](https://codecov.io/gh/tidymodels/parsnip/branch/master/graph/badge.svg)]
 status](https://www.r-pkg.org/badges/version/parsnip)](https://CRAN.R-project.org/package=parsnip)
 [![Downloads](http://cranlogs.r-pkg.org/badges/parsnip)](https://cran.rstudio.com/package=parsnip)
 [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
-
 <!-- badges: end -->
 
 ## Introduction
@@ -141,7 +140,7 @@ rand_forest(mtry = 10, trees = 2000) %>%
   fit(mpg ~ ., data = mtcars)
 #> parsnip model object
 #> 
-#> Fit time:  69ms 
+#> Fit time:  71ms 
 #> Ranger result
 #> 
 #> Call:
@@ -155,8 +154,8 @@ rand_forest(mtry = 10, trees = 2000) %>%
 #> Target node size:                 5 
 #> Variable importance mode:         impurity 
 #> Splitrule:                        variance 
-#> OOB prediction error (MSE):       5.815633 
-#> R squared (OOB):                  0.839896
+#> OOB prediction error (MSE):       5.911312 
+#> R squared (OOB):                  0.837262
 ```
 
 A list of all `parsnip` models across different CRAN packages can be
@@ -167,12 +166,21 @@ package.
 
 ## Contributing
 
-If you encounter a bug, please file a minimal reproducible example on
-[GitHub](https://github.com/tidymodels/parsnip/issues). For questions
-and other discussion, please use
-[community.rstudio.com](https://community.rstudio.com/).
-
-Please note that the parsnip project is released with a [Contributor
-Code of
+This project is released with a [Contributor Code of
 Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
+
+  - For questions and discussions about tidymodels packages, modeling,
+    and machine learning, please [post on RStudio
+    Community](https://rstd.io/tidymodels-community).
+
+  - If you think you have encountered a bug, please [submit an
+    issue](https://github.com/tidymodels/parsnip/issues).
+
+  - Either way, learn how to create and share a
+    [reprex](https://rstd.io/reprex) (a minimal, reproducible example),
+    to clearly communicate about your code.
+
+  - Check out further details on [contributing guidelines for tidymodels
+    packages](https://www.tidymodels.org/contribute/) and [how to get
+    help](https://www.tidymodels.org/help/).