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Do we have interpretability after using random forest?
Random Forest is a black box model you will lose interpretability after using it.
What are the advantages of Random Forests over decision tree?
A random forest is simply a collection of decision trees whose results are aggregated into one final result. Their ability to limit overfitting without substantially increasing error due to bias is why they are such powerful models. One way Random Forests reduce variance is by training on different samples of the data.
Can a random forest be used for interpretability?
It might seem surprising to learn that Random Forests are able to defy this interpretability-accuracy tradeoff, or at least push it to its limit. After all, there is an inherently random element to a Random Forest’s decision-making process, and with so many trees, any inherent meaning may get lost in the woods.
How are decisions made in a random forest?
When considering a decision tree, it is intuitively clear that for each decision that a tree (or a forest) makes there is a path (or paths) from the root of the tree to the leaf, consisting of a series of decisions, guarded by a particular feature, each of which contribute to the final predictions.
How are trees trained in a random forest?
Indeed, a forest consists of a large number of deep trees, where each tree is trained on bagged data using random selection of features, so gaining a full understanding of the decision process by examining each individual tree is infeasible.
Is there inherent meaning in a random forest?
After all, there is an inherently random element to a Random Forest’s decision-making process, and with so many trees, any inherent meaning may get lost in the woods. However, in precisely the same way that the trees work together to reduce predictive error, they work together to accurately represent feature importance.