### Description

The DecisionForest function uses a training data set to generate a
predictive model. You can input the model to the function
DecisionForestPredict (`td_decision_forest_predict_mle`

or `td_decision_forest_predict_sqle`

)
function, which uses it to make predictions.

### Usage

td_decision_forest_mle ( formula = NULL, data = NULL, maxnum.categorical = 20, tree.type = NULL, ntree = NULL, tree.size = NULL, nodesize = 1, variance = 0, max.depth = 12, mtry = NULL, mtry.seed = NULL, seed = NULL, outofbag = FALSE, display.num.processed.rows = FALSE, categorical.encoding = "graycode", data.sequence.column = NULL )

### Arguments

`formula` |
Required Argument. |

`data` |
Required Argument. |

`maxnum.categorical` |
Optional Argument. |

`tree.type` |
Optional Argument. |

`ntree` |
Optional Argument. |

`tree.size` |
Optional Argument. |

`nodesize` |
Optional Argument. |

`variance` |
Optional Argument. |

`max.depth` |
Optional Argument. |

`mtry` |
Optional Argument. |

`mtry.seed` |
Optional Argument. |

`seed` |
Optional Argument. |

`outofbag` |
Optional Argument. |

`display.num.processed.rows` |
Optional Argument. |

`categorical.encoding` |
Optional Argument. |

`data.sequence.column` |
Optional Argument. |

### Value

Function returns an object of class "td_decision_forest_mle" which is a named list containing object of class "tbl_teradata". Named list members can be referenced directly with the "$" operator using following names:

predictive.model

monitor.table

output

### Examples

# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("decisionforest_example", "housing_train", "boston") # Create object(s) of class "tbl_teradata". housing_train <- tbl(con, "housing_train") boston <- tbl(con, "boston") # Example 1 - td_decision_forest_out1 <- td_decision_forest_mle(formula = (homestyle ~ bedrooms + lotsize + gashw + driveway + stories + recroom + price + garagepl + bathrms + fullbase + airco + prefarea), data = housing_train, tree.type = "classification", ntree = 50, nodesize = 1, variance = 0.0, max.depth = 12, mtry = 3, mtry.seed = 100, seed = 100 ) # Example 2 - td_decision_forest_out2 <- td_decision_forest_mle(formula = (homestyle ~ bedrooms + lotsize + gashw + driveway + stories + recroom + price + garagepl + bathrms + fullbase + airco + prefarea), data = housing_train, tree.type = "classification", ntree = 50, nodesize = 2, max.depth = 12, mtry = 3, outofbag = TRUE ) # Example 3 - td_decision_forest_out3 <- td_decision_forest_mle(formula = (medv ~ indus + ptratio + lstat + black + tax + dis + zn + rad + nox + chas + rm + crim + age), data = boston, tree.type = "regression", ntree = 50, nodesize = 2, max.depth = 6, outofbag = TRUE )