Description Usage Arguments Details Value References Examples

View source: R/build_forest_predict.R

Builds Model of Random Forest or Multivariate Random Forest (when the number of output features > 1) using training samples and generates the prediction of testing samples using the inferred model.

1 | ```
build_forest_predict(trainX, trainY, n_tree, m_feature, min_leaf, testX)
``` |

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

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

`n_tree` |
Number of trees in the forest, which must be positive integer |

`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. If a node has less than or equal to min_leaf samples, then there will be no splitting in that node and this node will be considered as a leaf node. Valid input is positive integer, which is less than or equal to M (number of training samples) |

`testX` |
Testing samples of size Q x N, where Q is the number of testing samples and N is the number of features (Same number of features as training samples) |

Random Forest regression refers to ensembles of regression trees where a set of n_tree un-pruned regression trees are generated based on bootstrap sampling from the original training data. For each node, the optimal feature for node splitting is selected from a random set of m_feature from the total N features. The selection of the feature for node splitting from a random set of features decreases the correlation between different trees and thus the average prediction of multiple regression trees is expected to have lower variance than individual regression trees. Larger m_feature can improve the predictive capability of individual trees but can also increase the correlation between trees and void any gains from averaging multiple predictions. The bootstrap resampling of the data for training each tree also increases the variation between the trees.

In a node with training predictor features (X) and output feature vectors (Y), node splitting is done with the aim of selecting a feature from a random set of m_feature and threshold z to partition the node into two child nodes, left node (with samples < z) and right node (with samples >=z). In multivariate trees (MRF) node cost is measured as the sum of squares of the Mahalanobis distance where as in univariate trees (RF) node cost is measured as the Euclidean distance.

After the Model of the forest is built using training Input features (trainX) and output feature matrix (trainY), the Model is used to generate the prediction of output features (testY) for the testing samples (testX).

Prediction result of the Testing samples

[Random Forest] Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.

[Multivariate Random Forest] Segal, Mark, and Yuanyuan Xiao. "Multivariate random forests." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1.1 (2011): 80-87.

1 2 3 4 5 6 7 8 9 10 11 | ```
library(IntegratedMRF)
#Input and Output Feature Matrix of random data (created using runif)
trainX=matrix(runif(50*100),50,100)
trainY=matrix(runif(50*5),50,5)
n_tree=2
m_feature=5
min_leaf=5
testX=matrix(runif(10*100),10,100)
#Prediction size is 10 x 5, where 10 is the number
#of testing samples and 5 is the number of output features
Prediction=build_forest_predict(trainX, trainY, n_tree, m_feature, min_leaf, testX)
``` |

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