cancer dataset here we see that the feature used in the top split ("worst radius"is by far the most important feature this confirms our observation in analyzing the tree that the first level already separates the two classes fairly well howeverif feature has low feature_importanceit doesn' mean that this feature is uninformative it only means that the feature was not picked by the treelikely because another feature encodes the same information in contrast to the coefficients in linear modelsfeature importances are always positiveand don' encode which class feature is indicative of the feature importances tell us that "worst radiusis importantbut not whether high radius is indicative of sample being benign or malignant in factthere might not be such simple relationship between features and classas you can see in the following example (figures - and - )in[ ]tree mglearn plots plot_tree_not_monotone(display(treeout[ ]feature importances supervised learning