Aug 26, 2021 · Using caret, resampling with random forest models is automatically done with different mtry values. There are other functions out there, like tuneRF() that indicate some best guess mtry …

Typically the one restriction on random forest is that your number of features should be quite big - the first step of RF is to choose 1/3n or sqrt (n) features to construct a tree (depending on task, …

I've been playing around with random forests for regression and am having difficulty working out exactly what the two measures of importance mean, and how they should be interpreted. The importanc...

I understood that Random Forest and Extremely Randomized Trees differ in the sense that the splits of the trees in the Random Forest are deterministic whereas they are random in the case of an Extr...

The forest chooses the classification having the most votes (over all the trees in the forest). Another short definition of Random Forest: A random forest is a meta estimator that fits a number of decision …

Aug 20, 2021 · I'm currently training two separate Random Forest classifier models using a dataset where the target feature is imbalanced (fraud): RF 1 is trained on the imbalanced data and RF 2 is …

Sep 17, 2015 · Theoretically, Random Forest is ideal as it is commonly assumed and described by Breiman and Cuttler. In practice, it is very good but far from ideal. Therefore, these questions are …

Sep 9, 2021 · What's the difference, if any at all, between max_depth and max_leaf_nodes in sklearn's RandomForestClassifier for a simple binary classification problem? If the model always grows trees …

May 23, 2018 · 13 How many samples does each tree of a random forest use to train in sci-kit learn the implementation of Random Forest Regression? And, how does the number of samples change when …

Aug 15, 2014 · To avoid over-fitting in random forest, the main thing you need to do is optimize a tuning parameter that governs the number of features that are randomly chosen to grow each tree from the …