问题：pyspark.ml.regression.RandomForestRegressor预测默认情况下是离散输出，对应于与输入更好匹配的叶子。它不会插在两个最接近的叶子之间，这是我想要的行为。
问题：如何设置pyspark.ml.regression.RandomForestRegressor对输出进行插值？
我在这里找不到这样的选择： class pyspark.ml.regression.RandomForestRegressor
问题重现：请遵循本教程： MLlib: Main Guide
from pyspark.ml import Pipeline
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.feature import VectorIndexer
from pyspark.ml.evaluation import RegressionEvaluator
# Load and parse the data file, converting it to a DataFrame.
data = spark.read.format("libsvm").load("/FileStore/tables/sample_libsvm_data.txt")
# Automatically identify categorical features, and index them.
# Set maxCategories so features with > 4 distinct values are treated as continuous.
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])
# Train a RandomForest model.
rf = RandomForestRegressor(featuresCol="indexedFeatures")
# Chain indexer and forest in a Pipeline
pipeline = Pipeline(stages=[featureIndexer, rf])
# Train model. This also runs the indexer.
model = pipeline.fit(trainingData)
# Make predictions.
predictions = model.transform(testData)
# Select example rows to display.
predictions.select("prediction", "label", "features").show(10)
输出
+----------+-----+--------------------+
|prediction|label| features|
+----------+-----+--------------------+
| 0.0| 0.0|(692,[95,96,97,12...|
| 0.0| 0.0|(692,[121,122,123...|
| 0.0| 0.0|(692,[122,123,148...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.2| 0.0|(692,[125,126,127...|
| 0.05| 0.0|(692,[126,127,128...|
| 0.05| 0.0|(692,[127,128,129...|
+----------+-----+--------------------+