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What is the importance of randomForest in R?
The randomForest package in R has two measures of importance. One is “total decrease in node impurities from splitting on the variable, averaged over all trees.”
What is the outcome of the random forest?
In this instance, the outcome is whether a person has an income above or below $50,000. There are two measures of importance given for each variable in the random forest. The first measure is based on how much the accuracy decreases when the variable is excluded. This is further broken down by outcome class.
Which is better supervised learning or random forest?
Random forests ™ are great. They are one of the best “black-box” supervised learning methods. If you have lots of data and lots of predictor variables, you can do worse than random forests. They can deal with messy, real data. If there are lots of extraneous predictors, it has no problem.
How are predictors used in a random forest?
Random forests use out-of-bag (OOB) samples to measure prediction accuracy. In my experience, it does a pretty good job of finding the most important predictors, but it has issues with correlated predictors. For example, I was working on a problem where I was predicting the price that electricity trades.
How to interpret the output of a random forest?
In addition to the great answers above, I found interesting another instrument designed to explore the general outputs of a random forest: function explain_forest the package randomForestExplainer. See here for further details. Please, note: localImp has to be set as TRUE, otherwise the explain_forest will quit with an error
What happens if a variable is not important in a random forest?
The idea is that if the variable is not important (the null hypothesis), then rearranging the values of that variable will not degrade prediction accuracy. Random forests use out-of-bag (OOB) samples to measure prediction accuracy.
Which is the best model for random forest?
For values maxnodes: 80 and ntree: 900, the model seems to perform best. We would now use these parameters in the final model. For this algorithm, we used all available diamond features, but some of them contain more predictive power than others. Let’s build the plot with features list on the y axis.