grakel.cross_validate_Kfold_SVM#
- grakel.cross_validate_Kfold_SVM(K, y, n_iter=10, n_splits=10, C_grid=None, random_state=None, scoring='accuracy', fold_reduce=None)[source][source]#
Cross Validate a list of precomputed kernels with an SVM.
- Parameters:
- Klist
A list that must contain either numpy arrays or iterables of numpy arrays.
- ylist
List of lists that for every element of K contains numbers of score for all iterations.
- n_iterint
Number of iteration for the K-Fold.
- n_splitsint
Number of splits for the K-Fold.
- C_gridnumpy array or list, default=None
A list of C parameters for the SVM to choose from. By default, it is set to 10**-7, …, 10**7, divided by the number of examples
- random_stateRandomState or int, default=None
A random number generator instance or an int to initialize a RandomState as a seed.
- fold_reducecallable or None
A function that summarizes information between all folds. Input must be a list of n_splits elements corresponding to scoring. If None default is np.mean.
- scoringstring, callable, list/tuple, dict or None, default: None
As in
scoringin sklearn.model_selection.GridSearchCV.
- Returns:
- outlist
A list that contains a list of the reduced folds for each iteration, for each primary element of K.
