diff options
Diffstat (limited to 'docs/ml-ensembles.md')
| -rw-r--r-- | docs/ml-ensembles.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md index 58f566c9b4..ce15f5e646 100644 --- a/docs/ml-ensembles.md +++ b/docs/ml-ensembles.md @@ -195,7 +195,7 @@ import org.apache.spark.ml.feature.*; import org.apache.spark.sql.DataFrame; // Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read.format("libsvm") +DataFrame data = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); // Index labels, adding metadata to the label column. @@ -384,7 +384,7 @@ import org.apache.spark.ml.regression.RandomForestRegressor; import org.apache.spark.sql.DataFrame; // Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read.format("libsvm") +DataFrame data = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); // Automatically identify categorical features, and index them. @@ -640,7 +640,7 @@ import org.apache.spark.ml.feature.*; import org.apache.spark.sql.DataFrame; // Load and parse the data file, converting it to a DataFrame. -DataFrame data sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt"); +DataFrame data sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); // Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. @@ -830,7 +830,7 @@ import org.apache.spark.ml.regression.GBTRegressor; import org.apache.spark.sql.DataFrame; // Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt"); +DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); // Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. @@ -1000,7 +1000,7 @@ SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample"); JavaSparkContext jsc = new JavaSparkContext(conf); SQLContext jsql = new SQLContext(jsc); -DataFrame dataFrame = sqlContext.read.format("libsvm") +DataFrame dataFrame = sqlContext.read().format("libsvm") .load("data/mllib/sample_multiclass_classification_data.txt"); DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345); |