RandomForestRegressorModel new

---

__init__(self, name=None)

Create a ‘new’ instance of a Random Forest Regressor model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

: <bound method AtkEntityType.__name__ of <trustedanalytics.rest.jsonschema.AtkEntityType object at 0x7f9e68702090>>

Random Forest [R64] is a supervised ensemble learning algorithm used to perform regression. A Random Forest Regressor model is initialized, trained on columns of a frame, and used to predict the value of each observation in the frame. This model runs the MLLib implementation of Random Forest [R65]. During training, the decision trees are trained in parallel. During prediction, the average over-all tree’s predicted value is the predicted value of the random forest.

footnotes

[R64]

https://en.wikipedia.org/wiki/Random_forest

[R65]

https://spark.apache.org/docs/1.3.0/mllib-ensembles.html