# Sklearn Random Forest feature importance error bars

I'm plotting my RF feature importance using the method shown here.

Where the error bars in the plot are defined by:

std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)


However, the resulting error bars are huge, and even range into the negative range. I'm not quite sure what my interpretation of this should be?. Does this mean that the inter-tree variation of this variable's importance is huge, and if so does that mean that the variable is in fact not a great predictor of the target variable?