Label | Explanation | Data Type |

Input Features
| The layer containing the dependent and independent variables. | Record Set |

Dependent Variable
| The numeric field containing the observed values to be modeled. | Field |

Model Type
| Specifies the type of data that will be modeled. - Continuous (Gaussian) — The dependent_variable is continuous. The Gaussian model will be used, and the tool will perform ordinary least squares regression. This is the default.
- Binary (Logistic) — The dependent_variable represents presence or absence. This can be either conventional 1s and 0s, or string values mapped to 0 or 1s in the Match Explanatory Variables parameter. The Logistic Regression model will be used.
- Count (Poisson) —The dependent_variable is discrete and represents events, for example, crime counts, disease incidents, or traffic accidents. The Poisson regression model will be used.
- Continuous (Gaussian) — The Dependent Variable is continuous. The Gaussian model will be used, and the tool will perform ordinary least squares regression. This is the default.
- Binary (Logistic) — The Dependent Variable represents presence or absence. This can be either conventional 1s and 0s, or string values mapped to 0 or 1s in the explanatory_variables_to_match parameter. The Logistic regression model will be used.
- Count (Poisson) —The Dependent Variable is discrete and represents events, for example, crime counts, disease incidents, or traffic accidents. The Poisson regression model will be used.
| String |

Explanatory Variable(s) | A list of fields representing independent explanatory variables in the regression model. | Field |

Output Features Name | The name of the feature class that will be created containing the dependent variable estimates and residuals. | String |

Generate Coefficient Table
(Optional) | Specifies whether an output table with coefficient (Boolean) values will be generated. - Checked—A table with coefficient values will be generated.
- Unchecked—A table with coefficient values will not be generated. This is the default.
| Boolean |

Input Prediction Features (Optional) | A layer containing features representing locations where estimates will be computed. Each feature in this dataset should contain values for all the explanatory variables specified. The dependent variable for these features will be estimated using the model calibrated for the input layer data. | Record Set |

Match Explanatory Variables
(Optional) | Matches the explanatory variables in the Input Prediction Features parameter to corresponding explanatory variables from the Input Features parameter. | Value Table |

Map Dependent Variables
(Optional) | Two strings representing the values used to map to 0 (absence) and 1 (presence) for binary regression. By default, 0 and 1 will be used. For example, to predict an arrest with field values of Arrest and No Arrest, you would enter No Arrest for False Value (0) and Arrest for True Value (1). | Value Table |

Data Store
(Optional) | Specifies the ArcGIS Data Store where the output will be saved. The default is Spatiotemporal big data store. All results stored in a spatiotemporal big data store will be stored in WGS84. Results stored in a relational data store will maintain their coordinate system. - Spatiotemporal big data store —Output will be stored in a spatiotemporal big data store. This is the default.
- Relational data store —Output will be stored in a relational data store.
| String |

### Derived Output

Label | Explanation | Data Type |

Output | The output feature service containing the dependent variable estimates for each input feature. | Record Set |

Output Predicted Features | An output layer containing the input variables and predicted explanatory values. | Record Set |

Table of Coefficients | An output table with coefficient values. | Record Set |