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[backport] [doc] Add missing document for pyspark ranker. (#8692) (#8990)

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Jiaming Yuan
2023-03-29 12:02:51 +08:00
committed by GitHub
parent f5f03dfb61
commit 365da0b8f4
3 changed files with 21 additions and 10 deletions
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doc/tutorials/spark_estimator.rst
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@@ -43,10 +43,10 @@ in spark estimator, and some parameters are replaced with pyspark specific param
such as `weight_col`, `validation_indicator_col`, `use_gpu`, for details please see
`SparkXGBRegressor` doc.
The following code snippet shows how to train a spark xgboost regressor model,
first we need to prepare a training dataset as a spark dataframe contains
"label" column and "features" column(s), the "features" column(s) must be `pyspark.ml.linalg.Vector`
type or spark array type or a list of feature column names.
The following code snippet shows how to train a spark xgboost regressor model, first we
need to prepare a training dataset as a spark dataframe contains "label" column and
"features" column(s), the "features" column(s) must be ``pyspark.ml.linalg.Vector`` type
or spark array type or a list of feature column names.
.. code-block:: python
@@ -54,10 +54,10 @@ type or spark array type or a list of feature column names.
xgb_regressor_model = xgb_regressor.fit(train_spark_dataframe)
The following code snippet shows how to predict test data using a spark xgboost regressor model,
first we need to prepare a test dataset as a spark dataframe contains
"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
type or spark array type.
The following code snippet shows how to predict test data using a spark xgboost regressor
model, first we need to prepare a test dataset as a spark dataframe contains "features"
and "label" column, the "features" column must be ``pyspark.ml.linalg.Vector`` type or
spark array type.
.. code-block:: python