Contents
What does MTRY mean in random forest?
Number of variables randomly
mtry: Number of variables randomly sampled as candidates at each split. ntree: Number of trees to grow.
Is random forest unbiased?
Features of Random Forests It runs efficiently on large data bases. It can handle thousands of input variables without variable deletion. It gives estimates of what variables are important in the classification. It generates an internal unbiased estimate of the generalization error as the forest building progresses.
What happens when a random forest is overfitting?
If lot of your features are redundant, then when you perform the splits in the nodes of the trees, the algorithm may often only choose very poor features, which makes your model just chase some noise in your data. It is wise to look at the variable importance of the forest to try to identify if you have features that are maybe not relevant.
How big should a random forest be for mtry?
When training a random forest model, you should also grow a large enough forest. Perhaps 100 trees is not a big enough forest. Try growing a bigger forest in addition to optimize mtry. You need not worry about the size of the forest leading to over-fitting. Actually, the bigger the forest, the better (although there are diminishing returns).
Why does mtry influence out of sample prediction error?
Use five- or ten-fold cross-validation for this. The reason why mtry could influence out-of-sample prediction error is that when you grow larger trees, the trees are going to be more correlated with one another. When training a random forest model, you should also grow a large enough forest.
How to optimize tuning parameter in random forest?
When training a random forest model, you need to optimize the tuning parameter mtry, which is the number of features randomly selected for each tree. Use five- or ten-fold cross-validation for this.