[systemds] branch main updated: [MINOR][DOC] xgboost function y parameter correct usage (#1532)

[ja...@apache.org]

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The following commit(s) were added to refs/heads/main by this push:
     new 151a32f  [MINOR][DOC] xgboost function y parameter correct usage (#1532)
151a32f is described below

commit 151a32f9ef9655a0fd8d7fb298c2cd83a997d165
Author: Janardhan Pulivarthi <j1...@protonmail.com>
AuthorDate: Tue Feb 1 11:42:41 2022 +0530

    [MINOR][DOC] xgboost function y parameter correct usage (#1532)
---
 docs/site/builtins-reference.md | 6 +++---
 scripts/builtin/xgboost.dml     | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/site/builtins-reference.md b/docs/site/builtins-reference.md
index 5fa8a2f..1073ed7 100644
--- a/docs/site/builtins-reference.md
+++ b/docs/site/builtins-reference.md
@@ -2419,7 +2419,7 @@ M = xgboost(X = X, y = y, R = R, sml_type = 1, num_trees = 3, learning_rate = 0.
 | NAME                  | TYPE           | DEFAULT  | Description |
 | :------               | :------------- | -------- | :---------- |
 | X                     | Matrix[Double] |   ---    | Feature matrix X; categorical features needs to be one-hot-encoded |
-| Y                     | Matrix[Double] |   ---    | Label matrix Y |
+| y                     | Matrix[Double] |   ---    | Label matrix y |
 | R                     | Matrix[Double] |   ---    | Matrix R; 1xn vector which for each feature in X contains the following information |
 |                       |                |          |   - R[,2]: 1 (scalar feature) |
 |                       |                |          |   - R[,1]: 2 (categorical feature) |
@@ -2448,7 +2448,7 @@ Y = matrix("1.0
             7.0
             8.0", rows=5, cols=1)
 R = matrix("1.0 1.0 1.0 1.0 1.0", rows=1, cols=5)
-M = xgboost(X = X, Y = Y, R = R)
+M = xgboost(X = X, y = Y, R = R)
 ```
 
 
@@ -2499,6 +2499,6 @@ Y = matrix("1.0
             7.0
             8.0", rows=5, cols=1)
 R = matrix("1.0 1.0 1.0 1.0 1.0", rows=1, cols=5)
-M = xgboost(X = X, Y = Y, R = R, num_trees = 10, learning_rate = 0.4)
+M = xgboost(X = X, y = Y, R = R, num_trees = 10, learning_rate = 0.4)
 P = xgboostPredictRegression(X = X, M = M, learning_rate = 0.4)
 ```
diff --git a/scripts/builtin/xgboost.dml b/scripts/builtin/xgboost.dml
index b0df6a3..6cd61ed 100644
--- a/scripts/builtin/xgboost.dml
+++ b/scripts/builtin/xgboost.dml
@@ -27,7 +27,7 @@
 # NAME          TYPE              DEFAULT     MEANING
 # ----------------------------------------------------------------------------------------------------------------------
 # X             Matrix[Double]    ---         Feature matrix X; note that X needs to be both recoded and dummy coded
-# Y             Matrix[Double]    ---         Label matrix Y; note that Y needs to be both recoded and dummy coded
+# y             Matrix[Double]    ---         Label matrix y; note that y needs to be both recoded and dummy coded
 # R             Matrix[Double]    Matrix      Matrix R; 1xn vector which for each feature in X contains the following information
 #                                             - R[,1]: 1 (scalar feature)
 #                                             - R[,2]: 2 (categorical feature)