Description Usage Arguments Details Value Examples

Split of the training samples of the parent node into the child nodes based on the feature and threshold that produces the minimum cost

1 | ```
splitt(X, Y, m_feature, Index, Inv_Cov_Y, Command, ff)
``` |

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

`Y` |
Output Training response of size M x T, M is the number of samples and T is the number of output responses |

`m_feature` |
Number of randomly selected features considered for a split in each regression tree node. |

`Index` |
Index 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) |

`ff` |
Vector of m_feature from all features of X. This varies with each split |

At each node of a regression a tree, a fixed number of features (m_feature) are selected randomly to be considered for generating the split. Node cost for all selected features along with possible n-1 thresholds for n samples are considered to select the feature and threshold with minimum cost.

List with the following components:

`index_left` |
Index of the samples that are in the left node after splitting |

`index_right` |
Index of the samples that are in the right node after splitting |

`which_feature` |
The number of the feature that produces the minimum splitting cost |

`threshold_feature` |
The threshold value for the node split. A feature value less than or equal to the threshold will go to the left node and it will go to the right node otherwise. |

1 2 3 4 5 6 7 8 9 10 | ```
library(IntegratedMRF)
X=matrix(runif(20*100),20,100)
Y=matrix(runif(20*3),20,3)
m_feature=5
Index=1:20
Inv_Cov_Y=solve(cov(Y))
ff2 = ncol(X) # number of features
ff =sort(sample(ff2, m_feature))
Command=2#MRF, as number of output feature is greater than 1
Split_criteria=splitt(X,Y,m_feature,Index,Inv_Cov_Y,Command,ff)
``` |

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