svm_rf: SVM and RF Accuracies
In smanchan96/binspec: Binary Classification on Mass Spectra

Given a vector of neighbor values and a vector of the minimum number of peaks to be considered, this function finds the peak mz values for a data set by running binary_peaks using each of the neighbor vector values, runs SVM and RF on the peaks for each of the min_peak_count values, and returns the accuracies of each test in a table. The table's rows are the number of neighbors, and the columns are the min_peak_count values.

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svm_rf(list_of_dfs, labels, neighbors, min_peaks_percentage,
  error_window = 0.005, training = 1:length(list_of_dfs),
  multiple_cores = 1)

`list_of_dfs`	The first data frame of mz values and frequencies
`labels`	The labels of the two states the first data frame's values could be classified as
`neighbors`	A vector of the number of neighbors to be considered in the binary_peaks function
`min_peaks_percentage`	A vector of the minimum percent of times an m/z must be a peak to be considered in the classifier_accuracies function
`training`	Vector of the data frames to be used for training
`multiple_cores`	Number of cores to use
`errow_window`	A vector of percentage of nearby peaks that should be also labeled as peaks when one is found