tune_knn: DML Tuning with k-NN.

In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description

A tune function for a distance metric learning algorithm, using k-NN score as metric.

Usage

tune_knn(dml, X, y, n_neighbors, dml_params, tune_args, n_folds = 5,
  n_reps = 1, verbose = FALSE, seed = NULL, ...)

Arguments

X	array-like (N x d), training vector, where N is the number of samples, and d is the number of features.
y	array-like (N), labels vector, where N is the number of samples.
n_neighbors	Number of neighbors for k-NN.
dml_params	Additional keyword parameters for the distance metric learning algorithm, specified in a named list.
tune_args	A named list woth the parameters of the DML algorithm to tune. Each key has to be a keyword argument of the DML. The associated values have to be lists containing all the desired values for the tuning parameters.
n_folds	Number of folds for cross validation.
n_reps	Number of cross validations to do.
verbose	Boolean. If True, a console log will be printed.
seed	Integer. The seed used by the random number generator
...	Additional keyword arguments for k-NN.
The	string with the name of the DML Algorithm to tune. Select it from the items in the list 'dml'.

Value

A list with the following elements: - results: A dataframe whose entries are all the cases considered for the tune parameters, and with a single column that shows the cross validation score for each case. - best_performance: A pair with the best case obtained, together with its corresponding score. - best_dml: The DML Algorithm object that obtained the best result in the tuning. - detailed_results: A named list whose keys are all the possible cases, and each entry is the cross validation table for the corresponding case, containing the scores for every fold.

jlsuarezdiaz/rDML documentation built on May 24, 2019, 12:35 a.m.