Just a quick example from Max Kuhn's book.
featurePlot(x = iris[, 1:4], y = iris$Species, plot = "density", ## Pass in options to xyplot() to ## make it prettier scales = list(x = list(relation="free"), y = list(relation="free")), adjust = 1.5, pch = "|", layout = c(4, 1), auto.key = list(columns = 3))
I'm just curious about how to interpret these plots as to me they look like simple distribution plots. Is it right to assume that for a variable to be important, one would expect the density curves to be significantly different for the 3 classes, both in terms of the height (kurtosis) and placement (skewness)?
So if one predictor variable is significantly different in its height and placement from the other two, then that means that it has a high impact on the class outcome? In this case the setosa species can be easily identified through its petal length and width?