Contents
Do you need to set seed for random forest?
The answer is no. Your model gives a different result for each seed you use. This is a result of the non-deterministic nature of the model. By choosing a specific seed that maximizes the performance on the validation set means that you chose the “arrangement” that best fits this set.
How many trees does it take to grow a random forest?
Accordingly to this article in the link attached, they suggest that a random forest should have a number of trees between 64 – 128 trees. With that, you should have a good balance between ROC AUC and processing time.
Is there a way to make random forest predictions more interpretable?
There is a very straightforward way to make random forest predictions more interpretable, leading to a similar level of interpretability as linear models — not in the static but dynamic sense. Every prediction can be trivially presented as a sum of feature contributions, showing how the features lead to a particular prediction.
What does setting up the seed do in random forest?
I know that seed is set in general is used so that we can reproduce the same result. But, what does setting up the seed actually do in random forest part. Does it change any of the arguments of randomForest () function in R like nTree or sampSize.
What to know about a random forest model?
What if, after a transaction is classified as fraudulent, the analyst would like to know why the model made this decision, i.e. how much each feature contributed to the final outcome? Or what if a random forest model that worked as expected on an old data set, is producing unexpected results on a new data set.
Why is the random forest a supervised problem?
This task is a supervised, regression machine learning problem because we have the labels (targets) we want to predict, and those labels are continuous values (in contrast to unsupervised learning where we do not have labels, or classification, where we are predicting discrete classes).