crf_caretmethod contains functionality to tune a crf model using caret.
Each list elment of
crf_caretmethod is a list of functions
which can be passed on to the
method argument of
caret::train to tune the hyperparameters of the crfsuite model.
If you want to tune the hyperparameters of a crfsuite model
crf_options and the
options argument of
crf), you can use the
In order to facilitate this tuning, an object called
crf_caretmethod has been made available.
crf_caretmethod is a list with 6 elements, where each of these 6 elements can be used in
tuning the CRF hyperparemeters by passing it on to the
method argument of the
train function of the
The list has elements 'default', 'lbfgs', 'l2sgd', 'averaged_perceptron', 'passive_aggressive' and 'arow'. Each list element corresponds to arguments that you need to tune for each
method as used in
lbfgs: Tuning across all hyperparameters for method lbfgs: L-BFGS with L1/L2 regularization
l2sgd: Tuning across all hyperparameters for method l2sgd: SGD with L2-regularization
averaged_perceptron: Tuning across all hyperparameters for method averaged-perceptron: Averaged Perceptron
passive_aggressive: Tuning across all hyperparameters for method passive-aggressive: Passive Aggressive
arow: Tuning across all hyperparameters for method arow: Adaptive Regularization of Weights (AROW)
default: Tune over the hyperparameters feature.minfreq, feature.possible_states, feature.possible_transitions, max_iterations. While tuning these, it uses the default hyperparameters for each method. This tuning allows you to compare the 5 methods.
For details on the hyperparameter definitions: see
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