Description Usage Arguments Details Value Examples

`SoftClassForest`

creates categorical Random Forests of Soft Decision Trees while returning
the fitted classification given by the majority vote of individual SDTs.

1 2 | ```
SoftClassForest(trainresponses, train, test, ntry, ntrees, depth,
bag = TRUE)
``` |

`trainresponses` |
A matrix or data frame of responses |

`train` |
A matrix or data frame consisting of all possible variables to attempt for the training set. |

`test` |
A matrix or data frame consisting of all possible variables to attempt for the test set. |

`ntry` |
A numeric of the number of variables from the |

`ntrees` |
A numeric of the number of SDTs to build in the Random Forest. |

`depth` |
A numeric of the number of the depth each SDT should be. Here this ends with |

`bag` |
Logical if Random Forests should be built with bootstrap aggregating (TRUE) or raw data (FALSE). |

`SoftClassForest`

individually fits a Random Forest for each possible classification response using `SoftForestPredFeeder`

function
one classification at a time. The result from each one of these SDTs is a fitted probability of `0`

or `1`

.
Once all classifications have a fitted probability, the observation is classified as the maximum a posteriori probability.
Given a Random Forest of SDTs, the final Random Forest classification goes to the majority vote from the SDTs.

A vector of the final classifications based on the Random Forest generated.

1 2 3 | ```
Responses = SoftClassMatrix(as.vector(iris$Species))
SoftClassForest(trainresponses = Responses, train = iris[,1:4], test = iris[,1:4],
ntry = 2, ntrees = 15, depth = 2, bag = TRUE)
``` |

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