Returns the predicted target using the Random Forest modeling (a way of training an ensemble of decision trees).


Predict using a stored training model.

rforest [size=INT] model=MODEL

Predict using a model trained based on subquery results.

rforest [size=INT] target=TARGET_FILED FIELD, ... [ SUBQUERY ]
Required Parameter
FIELD, ...
Fields as predictor variables for the Random Forest modeling.
Name of the Random Forest model. You can generate and train the Random Forest model by connecting to the Logpresso engine via CLI.
Field as a target variable for the Random Forest modeling.
Subquery that returns the data set for model training.
Optional Parameter
Number of trees within the random forest (default: 100)


This command returns the predicted value of the target field into the _guess field.


  1. Predict using the rforest_titanic model.

    # Download: https://raw.githubusercontent.com/logpresso/dataset/main/titanic/train.csv
    table titanic_test
    | rforest model=rforest_titanic
    | eval _guess = if(_guess=="0", "사망 ", "생존")
  2. Predict using a model trained based on the training data set returned from a subquery.

    table titanic_test
    | rforest target=Survived Pclass, Sex, Age, Fare, Embarked
        [ csvfile /test/train.csv
          | eval Age=double(Age), 
            Fare=double(Fare), CanbinLetter=nvl(substr(Cabin, 0, 1), "--"), 
            TicketType=if(isnull(long(Ticket)), substr (Ticket, 0, indexof(Ticket, " ")), "--")
          | rex field=Name ", (?<Title>[^.]+)" 
          | eval Survived = if(Survived=="0", " 사망 ", "생존")