Description Usage Arguments Details Value See Also Examples

Function to perform an held-out procedure for a single class with a kernel-based score method

1 2 3 |

`K` |
matrix. Kernel matrix or any valid symmetric matrix |

`ind.pos` |
indices of the positive examples of the training set. They are the indices the row of RW corresponding to positive examples of the training set |

`ind.test` |
indices of the examples of the test set. They are the indices the row of RW corresponding to examples of the test set |

`m` |
number of folds for the cross-validation on the training set |

`p` |
number of repeated cross-validations on the training set |

`alpha` |
vector of the quantiles to be tested |

`init.seed` |
nitial seed for the random generator (def: 0) |

`opt.fun` |
Function implementing the metric to select the optimal threshold. The F-score (compute.F) is the default. Available functions: - compute.F: F-score (default) - compute.acc:accuracy. Any function having two arguments representing the vector of predicted and true labels can be in principle used. |

`fun` |
function. It must be a kernel-based score method (default KNN.score) |

`...` |
optional arguments for the function fun |

Function to classify labels according to an hold-out procedure with a kernel-based score method. The optimal threshold for a given class is obtained by (possibly multiple) internal cross-validation on the training set. Scores of the held-out nodes are computed. Thresholds are computed on the training set by cross-validation and then are used to classify the held-out nodes in the test set. The optimal quantile and corresponding threshold are selected by internal cross-validation using the F-score as metrics. Note the test examples are given as indices of the rows of the input matrix RW.

a list with four components: A list with 4 components:

`labels` |
vector of the predicted labels for the test set(1 represent positive, 0 negative) |

`av.scores ` |
a vector with the scores computed on the test set. Elements of the vector av.scores correspond to ind.test rows of RW |

`opt.alpha ` |
the optimal quantile alpha |

`opt.thresh ` |
the optimal threshold |

`rw.kernel-methods`

, `Kernel functions`

, `ker.score.classifier.cv`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# Node label classification of the DrugBank category Penicillins
# on the Tanimoto chemical structure similarity network (1253 drugs)
# with eav-score with 1-step random walk kernel
# using held-out with 5-fold CV repeated 10 times on the training set
# to set the "optimal" threshold for classifiaction
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
labels <- DrugBank.Cat[,"Penicillins"];
ind.test <- 1:300;
ind.train <- 301:length(labels);
ind.pos <- which(labels==1);
ind.pos <- ind.pos[ind.pos>300];
K <- rw.kernel(DD.chem.data);
res <- ker.score.classifier.holdout(K, ind.pos, ind.test, m = 5, p = 10, fun = eav.score);
``` |

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