**the question**

Demonstrate the speed and accuracy of properly applied 'Random Forest' as a variable importance selection tool especially in handling very large data against alternative approaches such as support vector machines (svm). In an ideal case it would be a pre-processor followed by a second method that is less "non-parametric".

I have given previous answers (link) that weakly answer this, but I think that a clean and complete answer is relevant for the learners in the field.

**motivation**:

There are several papers published [1,2,3,4] where the approach is described as "generally more accurate and **incomparably faster** (on massive in both dimensions datasets) than original Freidman’s MART and Breiman’s RF" and capable for tens of thousands of predictors (columns) of mixed type (categorical and numeric) and can include missing values, by tens of millions of samples (rows) when the "physics" are highly non-linear, multivariate, and noisy.

In these papers the fundamental tool is the 'Random Forest' as a pre-processor. The general approach is to use the Random Forest to determine the important variables (columns) then using only those columns, operate with a much more sophisticated method. In one case there is augmentation of the columns of the data to enhance determination of variable importance. There are a number of alternative post approaches including Least-square fits, and ANOVA.

I personally have found this approach to be very useful, both with high and low dimensional data. While it is published, I do not know that its utility has been demonstrated outside of a narrow part of the field, so I think many practitioners in the field do not know to use it.

While it is a heuristic argument, I think the fact that the majority of winning-est algorithms on kaggle are related to parallel ensembles (rf) and series ensembles (gbm) is a decent indication of the power of these families of methods.

**details:**

There should be two data-sets. The first should be a "decent toy" like the one shown on the demos tab here, and the second should be a decent "big-boy" data set like the pre-processed version of the HIVA data.

Methods should use "default" or "textbook" settings. Tuning outside of these is likely to result in less generalizable results.

Measures of interest should include both cpu time, and error.

*( note to moderators: I think a question like this, and its answer are important and will provide value from cv readers. I intend to provide an answer to this myself if no excellent answers are forthcoming. consider this – it is very basic, but keeps getting up-votes because folks keep finding value in it. This question is my follow-up to this meta question.)*

**Contents**hide

#### Best Answer

Note: this answer is incomplete.

**Toy Problem:**

This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (link, link) demo to be accessible for my intuition and learning. So do the folks at the Max Planck Institute for Biological Cybernetics.

The form of the "non-augmented" data is: $$ begin{bmatrix} Class & X & Y\ A& x_1 & y_1\ A& x_2 & y_2\ vdots & vdots & vdots\ B& x_n & y_n\ end{bmatrix}$$

The "physics" of the "good" class is a spiral starting at the origin while the bad class is uniformly random. The human eye can see that quickly. When evaluating "variable importance" we are trying to reduce the number of columns, but the non-augmented has no columns to reduce, thus we augment with random. There is the problem of overlap, it would be better to reclassify some of the uniform random within a range of "ideal" to class "A".

So here is the code that makes the "non-augmented" data:

`#housekeeping rm(list=ls()) #library library(randomForest) #for reproducibility set.seed(08012015) #basic n <- 1:2000 r <- 0.05*n +1 th <- n*(4*pi)/max(n) #polar to cartesian x1=r*cos(th) y1=r*sin(th) #add noise x2 <- x1+0.1*r*runif(min = -1,max = 1,n=length(n)) y2 <- y1+0.1*r*runif(min = -1,max = 1,n=length(n)) #append salt and pepper x3 <- runif(min = min(x2),max = max(x2),n=length(n)) y3 <- runif(min = min(y2),max = max(y2),n=length(n)) X <- c(x2,x3) Y <- c(y2,y3) myClass <- as.factor(c(as.vector(matrix(1,nrow=length(y2))), as.vector(matrix(2,nrow=length(y3))) )) #plot class "A" derivation plot(x1,y1,pch=18,type="l",col="Red", lwd=2) points(x2,y2,pch=18) points(x3,y3,pch=1,col="Blue") legend(x = 65,y=65, legend = c("true","sampled A","sampled B"), col = c("Red","Black","Blue"), lty = c(1,-1,-1), pch=c(-1,18,1)) `

Here is a plot of the non-augmented data.

Here is the code to augment the "toy" for variable importance detection, and assemble into a single data frame.

`#Create bad columns class of uniform randomized good columns x5 <- sample(x = X, size = length(X),replace = T) y5 <- sample(x = Y, size = length(Y),replace = T) #assemble data into frame data <- data.frame(myClass, c(X),c(Y),c(x5),c(y5) ) names(data) <- c("myclass","x","y","n1","n2") `

First a random forest (not yet with t-tests as in the Tuv reference) is used on all input columns to determine relative variable importance, and to get a sense of sufficient number of trees. It is assumed that more trees are required to get a decent fit using low importance data than with uniformly higher importance data.

`#train random forest - I like h2o, but this is textbook Breimann fit.rf_imp <- randomForest(data[2:5],data$myclass, ntree = 2000, replace=TRUE, nodesize = 1, localImp=T ) varImpPlot(fit.rf_imp) plot(fit.rf_imp) grid() importance(fit.rf_imp) `

The results for importance (in plot form) are:

The mean decrease in accuracy and mean decrease in gini have a consistent message: "n1 and n2 are low importance columns".

The results for the convergence plot are:

Although somewhat qualitative, it appears that some acceptable level of convergence has occurred by 500 trees. It is also worth noting that the converged error rate is about 22%. This leads to the inference that the "classification error" within the region of "A" is about 1 in 5.

The code for an updated forest, one not including low-importance columns, is:

`fit.rf <- randomForest(data[2:3],data$myclass, ntree = 500, replace=TRUE, nodesize = 1, localImp=T ) `

A plot of actual vs. predicted has excellent accuracy. Code to derive the plot follows:

`data2 <- predict(fit.rf,newdata=data[data$myclass==1,c(2,3,4,5)], type="response") #separate class "1" from training data idx1a <- which(data[,1]==1) #separate class "1" from the predicted data idx1b <- which(data2==1) #separate class "2" from training data idx2a <- which(data[,1]==2) #separate class "2" from the predicted data idx2b <- which(data2==2) #show the difference in classes before and after RF based filter #class "B" aka 2, uniform background plot(data[idx2a,2],data[idx2a,3]) points(data[idx2b,2],data[idx2b,3],col="Blue") #class "A" aka 1, red spiral points(data[idx1a,2],data[idx1a,3]) points(data[idx1b,2],data[idx1b,3],col="Red",pch=18) `

For a very simple toy problem, a basic randomForest has been used to determine importance of variables, and to attempt to classify "in" versus "out".

I have an older laptop. It is a Dell Latitude E-7440 with an i7-4600 and 16 GB of RAM running Windows 7. You might have something fancy, or something even older than mine. You could have different OS, R version, or hardware. Your results are likely to differ from mine in absolute scale, but relative scale should still be informative.

Here is the code I used to benchmark the "variable importance" random forest:

`res1 <- microbenchmark(randomForest(data[2:5],data$myclass, ntree = 2000, replace=TRUE, nodesize = 1, localImp=T ), times=100L) print(res1) `

and here is the code I used to benchmark the fit of a random forest to the important variables only:

`res2 <- microbenchmark(randomForest(data[2:3],data$myclass, ntree = 500, replace=TRUE, nodesize = 1, localImp=T ), times=100L) print(res2) `

The time-result for the variable importance was:

` min lq mean median uq max neval 1 9.323244 9.648383 9.967486 9.84808 10.05356 12.12949 100 `

Over 100 iterations the mean time-to-compute was 9.96 seconds. This is the "time to beat" for "incomparably faster" applied to the toy problem.

The time-result for the reduced model was:

` min lq mean median uq max neval 1.515134 1.598504 1.638809 1.634209 1.67372 2.038021 100 `

When computed over 100 iterations, the mean time-to-compute was 1.64 seconds. Running on the important data, and only for "reasonable" number of trees, reduced the run-time by about 84%.

INCOMPLETE.

References:

- http://www.statistik.uni-dortmund.de/useR-2008//slides/Strobl+Zeileis.pdf
- https://arxiv.org/pdf/1804.03515.pdf (updated 11/27/2018)

Awaiting:

random Forest on non-toy, with timing. HIVA is not the right data, even though I asked for it. I need an intermediate set.

random Forest + t-test solution on toy, with timing

- random Forest + t-test solution on non-toy, with timing
- svm solution on toy, with timing
- svm solution on non-toy, with timing

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