Multioutput regression and classification

MultiOutputClassifier/MultiOutputRegressor

Common

Input Data

	Inputs
*Target Data	Data Input
*Model Name	Name of the user-defined multi classifier model
Predict Output	If not empty, the model will additionally make prediction on Target Data and store the result here.
Merge Data	Predict Output will be created with predicted labels concatenated to Merge Data.
*x	User-selected columns to be x. One or more columns should be selected.
*y	User-selected columns to be y. Two or more columns should be selected.

Defining estimators

In Arguments, you have to define a classification estimator for MultiOutputClassifier and a regression estimator for MultiOutputRegressor.

About R-Flow estimators

[Task information of MultiOutputClassifier]

Workflow Example

R-Flow Task Example Video: MultiOutputRegressor

Python Package

MultiOutputClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html

MultiOutputRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html