Models RandomForestRegressorModel

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class RandomForestRegressorModel

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

Random Forest [R60] 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 [R61]. 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

[R60]

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

[R61]

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

Attributes

name	Set or get the name of the model object.

Methods

__init__(self[, name, _info])	Create a ‘new’ instance of a Random Forest Regressor model.
predict(self, frame[, observation_columns])	[ALPHA] Predict the values for the data points.
publish(self)	[BETA] Creates a scoring engine tar file.
train(self, frame, label_column, observation_columns[, num_trees, impurity, ...])	[ALPHA] Build Random Forests Regressor model.

__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 [R62] 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 [R63]. 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

[R62]

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

[R63]

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