  We investigate the importances of various dynamical features in predicting
the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF)
machine learning approach. We use a large sample of galaxy clusters from the
Three Hundred Project of hydrodynamical zoomed-in simulations, and construct
dynamical features from the raw data as well as from the corresponding mock
maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of
relying on the impurity based feature importance of the RF algorithm, we
directly use the out-of-bag (OOB) scores to evaluate the importances of
individual features and different feature combinations. Among all the features
studied, we find the virial ratio, $\eta$, to be the most important single
feature. The features calculated directly from the simulations and in
3-dimensions carry more information on the DS than those constructed from the
mock maps. Compared with the features based on X-ray or SZ maps, features
related to the centroid positions are more important. Despite the large number
of investigated features, a combination of up to three features of different
types can already saturate the score of the prediction. Lastly, we show that
the most sensitive feature $\eta$ is strongly correlated with the well-known
half-mass bias in dynamical modelling. Without a selection in DS, cluster halos
have an asymmetric distribution in $\eta$, corresponding to an overall positive
half-mass bias. Our work provides a quantitative reference for selecting the
best features to discriminate the DS of galaxy clusters in both simulations and
observations.
