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Regression Model Predictor

Description#

Regression allows to predict a continuous outcome (y) based on the value of one or more predictor variables (x).

Properties#

Input#

  • Algorithm Type – Select the classification algorithm for prediction. The value can be “RandomForestRegressor”, “DecisionTreeRegressor”, “LinearRegression”.
    • RandomForestRegressor: It combines multiple decision trees to determine the final output rather than rely on individual decision trees.

      Missing value

    • DecisionTreeRegressor: Decision trees are predictive models that use a set of binary rules to calculate a target value.
    • Linear Regression: It is a type of regression analysis where the number of independent variables is one, and the linear relationship between the independent(x) and dependent(y) variable.
  • Input Data – Data for predicting values.
  • Model Name – Generated model name for prediction.

Misc#

  • DisplayName – Add a display name to your activity.
  • Private – By default, activity will log the values of your properties inside your workflow. If private is selected, then it stops logging.

Optional#

  • Continue On Error – Specifies if the automation should continue even when the activity throws an error. This field only supports Boolean values (True, False). The default value is False.

    Note: If this activity is included in Try Catch and the value of this property is True, no error is caught when the project is executed

Output#

  • Result – Prediction value returned by the specified model.

Example#