height="6cm"}

Features
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Despite having indistinguishable performance in the prediction task, mRMR and DC-SIS do not select the same features. The difference in feature selection for each method caused by the jackknife is minimal, typically only one to four features change from fold to fold. However, between methods less than half of the features are shared for any given fold. The details for each dataset can be observed in Table \[tab:feat\].

For the PD dataset, mRMR has 35 features that were selected in every fold (for details, see Appendix). Similarly, DC-SIS has 42 features that were selected in every fold of the Leave-One-Subject-Out jackknife. Of these, the two procedures only agree on 12 features which are as follows:\
\
`    tqwt_stdValue_dec_12`\
`    tqwt_TKEO_std_dec_12`\
`    tqwt_entropy_log_dec_12`\
`    tqwt_entropy_shannon_dec_11`\
`    mean_MFCC_2nd_coef`\
`    tqwt_TKEO_std_dec_11`\
`    std_delta_delta_log_energy`\
`    std_9th_delta_delta`\
`    std_8th_delta_delta`\
`    tqwt_energy_dec_12`\
`    std_7th_delta_delta`\
`    std_6th_delta_delta`\
Of these 12, six are tunable-Q wavelet properties, five are standard wavelet properties, and one is a Mel Frequency Cepstral Coefficient. These twelve are the most crucial for making predictions, but are insufficient on their own. DC-SIS and mRMR disagree on how to augment these twelve for the best predictions but end up with the same predictive 