Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function selects optimal trees for class membership probability estimation from a total of `t.initial`

trees grown by random forest. Number of trees in the initial set, `t.initial`

, is specified by the user. If not specified then the default `t.initial = 1000`

is used.

1 2 |

`XTraining` |
An |

`YTraining` |
A vector of length |

`p` |
Percent of the best |

`t.initial` |
Size of the initial set of probability estimation trees. |

`nf` |
Number of features to be sampled for spliting the nodes of the trees. If equal to |

`ns` |
Node size: Minimal number of samples in the nodes. If equal to |

`info` |
If |

Large values are recommended for `t.initial`

for better performance as possible under the available computational resources.

A trained object consisting of the selected trees.

Prior action needs to be taken in case of missing values as the fuction can not handle them at the current version.

Zardad Khan <zkhan@essex.ac.uk>

Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). Ensemble of optimal trees, random forest and random projection ensemble classification. Advances in Data Analysis and Classification, 1-20.

Liaw, A. and Wiener, M. (2002) “Classification and regression by random forest” R news. 2(3). 18–22.

`Predict.OTProb`

, `OTReg`

, `OTClass`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
#load the data
data(Body)
data <- Body
#Divide the data into training and test parts
set.seed(9123)
n <- nrow(data)
training <- sample(1:n,round(2*n/3))
testing <- (1:n)[-training]
X <- data[,1:24]
Y <- data[,25]
#Train OTClass on the training data
Opt.Trees <- OTProb(XTraining=X[training,],YTraining = Y[training],t.initial=200)
#Predict on test data
Prediction <- Predict.OTProb(Opt.Trees, X[testing,],YTesting=Y[testing])
#Objects returned
names(Prediction)
Prediction$Brier.Score
Prediction$Estimated.Probabilities
``` |

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