plot_feature_importances_cancer(forestfigure - feature importances computed from random forest that was fit to the breast cancer dataset as you can seethe random forest gives nonzero importance to many more features than the single tree similarly to the single decision treethe random forest also gives lot of importance to the "worst radiusfeaturebut it actually chooses "worst perimeterto be the most informative feature overall the randomness in building the random forest forces the algorithm to consider many possible explanationsthe result being that the random forest captures much broader picture of the data than single tree strengthsweaknessesand parameters random forests for regression and classification are currently among the most widely used machine learning methods they are very powerfuloften work well without heavy tuning of the parametersand don' require scaling of the data essentiallyrandom forests share all of the benefits of decision treeswhile making up for some of their deficiencies one reason to still use decision trees is if you need compact representation of the decision-making process it is basically impossible to interpret tens or hundreds of trees in detailand trees in random forests tend to be deeper than decision trees (because of the use of feature subsetsthereforeif you need to summarize the prediction making in visual way to nonexpertsa single decision tree might be better choice while building random forests on large datasets might be somewhat time consumingit can be parallelized across multiple cpu supervised machine learning algorithms