![]() Example Python Codeįirst for some set up, I import the libraries I am using, and read in the emergency room use data: import numpy as npįrom nhanes_vardef import * #variable definitionsįrom sklearn.ensemble import RandomForestRegressorįrom sklearn.model_selection import train_test_split So I am interested here in generating prediction intervals for the typical time it takes to be served in an ER to see if my visit was normal or outlying. I have code to follow along here, but I will walk through it in this post (that code has some nice functions for data definitions for the NHAMCS data).Īt work I am working on a project related to unnecessary emergency room visits, and I actually went to the emergency room in December (for a Kidney stone). To illustrate, I will use a dataset of emergency room visits and time it took to see a MD/RN/PA, the NHAMCS data. And based on the residual distribution, one can generate forecast intervals (very similar to Duan’s smearing). In short, this approach to generate prediction intervals from random forests relies on out of bag error metrics (it is sort of like a for free hold out sample based on the bootstrapping approach random forest uses). Imagine I give you the choice of buy a home valuated at 150k - 300k after flipped vs a home valuated at 230k-250k, the upside for the first is higher, but it is more risky. I may want to generate prediction intervals that cover the value 90% of the time, and only base my decisions to buy based on the much lower value (if you are more risk averse). Prediction intervals are often of more interest for predictive modeling, say I am predicting future home sale value for flipping houses. They are actually easier to interpret than confidence intervals, you expect the prediction interval to cover the observations a set percentage of the time (whereas for confidence intervals you have to define some hypothetical population of multiple measures). Imagine we use that equation to make a prediction though, y_hat = B1*(x=10), here prediction intervals are errors around y_hat, the predicted value. So first what is a prediction interval? I imagine folks are more familiar with confidence intervals, say we have a regression equation y = B1*x + e, you often generate a confidence interval around B1. Those Cross Validated posts have R code, figured it would be good to illustrate in python code how to generate these prediction intervals using random forests. A recent set of answers on StackExchange show a different approach – apparently the individual tree approach tends to be too conservative (coverage rates higher than you would expect). (See this prior python post of mine for getting the individual trees). A significant contribution of this study is the ability to assess different variable selection techniques in the setting of random forest classification in order to identify preferable methods based on applications in expert and intelligent systems.Ĭlassification feature reduction random forest variable selection.I previously knew about generating prediction intervals via random forests by calculating the quantiles over the forest. For datasets with many predictors, the methods implemented in the R packages varSelRF and Boruta are preferable due to computational efficiency. Based on our study, the best variable selection methods for most datasets are Jiang's method and the method implemented in the VSURF R package. We compare random forest variable selection methods for different types of datasets (datasets with binary outcomes, datasets with many predictors, and datasets with imbalanced outcomes) and for different types of methods (standard random forest versus conditional random forest methods and test based versus performance based methods). Using 311 classification datasets freely available online, we evaluate the prediction error rates, number of variables, computation times and area under the receiver operating curve for many random forest variable selection methods. Several variable selection methods exist for the setting of random forest classification however, there is a paucity of literature to guide users as to which method may be preferable for different types of datasets. Often in prediction modeling, a goal is to reduce the number of variables needed to obtain a prediction in order to reduce the burden of data collection and improve efficiency. Random forest classification is a popular machine learning method for developing prediction models in many research settings. ![]()
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