cl_poisson_naive_bayes: A Poisson Naive Bayes classifier (CL)

In emeyers/NDTr: The Neural Decoding Toolbox in R

Description

An implementation of a Poisson Naive Bayes classifier.

Usage

cl_poisson_naive_bayes()

Details

This classifier object implements a Poisson Naive Bayes classifier. The classifier works by learning the expected number of occurrences (denoted lambda) for each feature and each class by taking the average of the training data over all trials (separately for each feature and each class). To evaluate whether a given test point belongs to class i, the log of the likelihood function is calculated using the lambda values as parameters of Poisson distributions (i.e., there is a separate Poisson distribution for each feature, that is based on the lambda value for that feature). The overall likelihood value is calculated by multiplying the probabilities for each neuron together (i.e,. Naive Bayes classifiers assume that each feature is independent), or equivalently, adding the log of the probabilities for each feature together. The class with the highest likelihood value is chosen as the predicted label, and the decision values are the log likelihood values.

Note: this classifier uses spike counts, so the binned data must be converted to use this classifier, for example, if you are using the basic_DS data source, then use_count_data = TRUE should be set in the constructor. Also, preprocessors that convert the data into values that are not integers should not be used, for example, the fp_zscore should not be used with this classifier.

Like all classifiers, this classifier learning a model based on training data and then makes predictions on new test data.

See Also

Other classifier: cl_max_correlation(), cl_svm()

Examples

# running a basic decoding analysis using the cl_max_correlation

data_file <- system.file(file.path("extdata", "ZD_150bins_50sampled.Rda"),
                         package = "NDTr")
ds <- ds_basic(data_file, "stimulus_ID", 18, use_count_data = TRUE)
fps <- list()

cl <- cl_poisson_naive_bayes()
cv <- cv_standard(ds, cl, fps)
## Not run: 
DECODING_RESULTS <- run_decoding(cv)

## End(Not run)

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