get_predictions: A classifier (CL) method to train the CL and returns...

In emeyers/NDTr: The Neural Decoding Toolbox in R

Description

get_predictions takes a training set and a test set of data. It trains the CL object on the training set and returns the predictions of the on the test set. This is a generic function that must be implemented by all CL objects. This method should not be called directly but instead it is used internally by the cross-validator (CV) object.

Usage

get_predictions(cl_obj, training_set, test_set)

Arguments

cl_obj	The classifier object.
training_set	The training set data from one time bin. This is a data frame where the rows correspond to data from a given trial. There must be a column called train_labels that has the labels of what occurred on each trial. The rest of the columns correspond to the neural activity of a particular site on each trial (and typically have names like site_0001, site_0002, etc).
test_set	The test set data from all times. This is a data frame where the rows correspond to data from a given trial. There must be a column called time_bin that contains a label indicating the time point that a row (test point) came from. The rest of the columns correspond to the neural activity of a particular site on each test trial (and typically have names like site_0001, site_0002, etc).

Value

This method returns a data frame where each row corresponds to a prediction for one of the test points. The columns in this data frame are:

• test_time: The time bin a test point came from.

• actual_labels: The actual labels for what happened on a trial.

• predicted_labels: The predictions that classifier made.

• decision_vals.___: A set of columns with the decision values for each class.

See Also

cl_max_correlation(), cl_poisson_naive_bayes(), cl_svm()

emeyers/NDTr documentation built on Aug. 8, 2020, 3:41 p.m.