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Fig. 3 | Animal Biotelemetry

Fig. 3

From: Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

Fig. 3

Variable importance for classifying female grey seal behaviour. Ten feature variables with the highest mean decrease in Gini, indicating the relative importance of each of the feature variables within the random forest model classifying 6 behaviours in lactating grey seals using head-mounted accelerometers (2015, 50 Hz). Top feature variables included static acceleration components (stZ, stY, stX) and their derivatives, pitch and roll, as well as smoothed VeDBA and elements of power spectrum densities (PSD1, PSD2) in the X and Y dimensions as defined in Table 4

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