## What does randomForest do in R?

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Random Forest in R Programming is an ensemble of decision trees. It builds and combines multiple decision trees to get more accurate predictions. It’s a non-linear classification algorithm. Each decision tree model is used when employed on its own.

**What is MTRY randomForest R?**

Direct from the help page for the randomForest() function in R: mtry: Number of variables randomly sampled as candidates at each split. ntree: Number of trees to grow.

### What is randomForest package?

The package “randomForest” has the function randomForest() which is used to create and analyze random forests.

**What does R do with Na?**

Missing data in R appears as NA. NA is not a string or a numeric value, but an indicator of missingness.

## What is IncMSE in random forest?

%IncMSE is the most robust and informative measure. It is the increase in mse of predictions(estimated with out-of-bag-CV) as a result of variable j being permuted(values randomly shuffled).

**What is variable importance in random forest?**

by Jake Hoare. After training a random forest, it is natural to ask which variables have the most predictive power. Variables with high importance are drivers of the outcome and their values have a significant impact on the outcome values.

### What does trainControl do in R?

5.5. 4 The trainControl Function. The function trainControl generates parameters that further control how models are created, with possible values: method : The resampling method: “boot” , “cv” , “LOOCV” , “LGOCV” , “repeatedcv” , “timeslice” , “none” and “oob” .

**How do you cite a randomForest package?**

randomForest citation info. Liaw A, Wiener M (2002). “Classification and Regression by randomForest.” R News, 2(3), 18-22. https://CRAN.R-project.org/doc/Rnews/.

## Is NA remove R?

If you include the NA value in a calculation it will result in an NA value. While this may be okay sometimes in other cases you need a number. The two remove NA values in r is by the na. omit() function that deletes the entire row, and the na.

**What is %IncMSE and IncNodePurity?**

Mean Decrease Accuracy (%IncMSE) – This shows how much our model accuracy decreases if we leave out that variable. Mean Decrease Gini (IncNodePurity) – This is a measure of variable importance based on the Gini impurity index used for the calculating the splits in trees.

### What is IncNodePurity in random forest?

IncNodePurity relates to the loss function which by best splits are chosen. The loss function is mse for regression and gini-impurity for classification. More useful variables achieve higher increases in node purities, that is to find a split which has a high inter node ‘variance’ and a small intra node ‘variance’.