Description Usage Arguments Details Value

View source: R/build_single_tree.R

Build a Univariate Regression Tree (for generation of Random Forest (RF) ) or Multivariate Regression Tree ( for generation of Multivariate Random Forest (MRF) ) using the training samples, which is used for the prediction of testing samples.

1 | ```
build_single_tree(X, Y, m_feature, min_leaf, Inv_Cov_Y, Command)
``` |

`X` |
Input Feature matrix of M x N, M is the number of training samples and N is the number of input features |

`Y` |
Output Feature matrix of M x T, M is the number of training samples and T is the number of ouput features |

`m_feature` |
Number of randomly selected features considered for a split in each regression tree node, which must be positive integer and less than N (number of input features) |

`min_leaf` |
Minimum number of samples in the leaf node, which must be positive integer and less than or equal to M (number of training samples) |

`Inv_Cov_Y` |
Inverse of Covariance matrix of Output Response matrix for MRF(Input [0 0;0 0] for RF) |

`Command` |
1 for univariate Regression Tree (corresponding to RF) and 2 for Multivariate Regression Tree (corresponding to MRF) |

The regression tree structure is represented as a list of lists. For a non-leaf node, it contains the splitting criteria (feature for split and threshold) and for a leaf node, it contains the output responses for the samples contained in the leaf node.

Model of a single regression tree (Univariate or Multivariate Regression Tree). An example of the list of the non-leaf node:

`Flag for determining node status; leaf node (1) or branch node (0)` |
1 |

`Index of samples for the left node` |
int [1:34] 1 2 4 5 ... |

`Index of samples for the right node` |
int [1:16] 3 6 9 ... |

`Feature for split` |
int 34 |

`Threshold values for split, average them` |
num [1:3] 0.655 0.526 0.785 |

`List number for the left and right nodes` |
num [1:2] 2 3 |

An example of the list of the leaf node:

`Output responses` |
num[1:4,1:5] 0.0724 0.1809 0.0699 ... |

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