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Actually, I thought I had understood what one can show a with partial dependence plot, but using a very simple hypothetical example, I got rather puzzled. In the following chunk of code I generate three independent variables (a, b, c) and one dependent variable (y) with c showing a close linear relationship with y, while a and b are uncorrelated with y. I make a regression analysis with a boosted regression tree using the R package gbm:

a <- runif(100, 1, 100)
b <- runif(100, 1, 100)
c <- 1:100 + rnorm(100, mean = 0, sd = 5)
y <- 1:100 + rnorm(100, mean = 0, sd = 5)
par(mfrow = c(2,2))
plot(y ~ a); plot(y ~ b); plot(y ~ c)
Data <- data.frame(matrix(c(y, a, b, c), ncol = 4))
names(Data) <- c("y", "a", "b", "c")
library(gbm)
gbm.gaus <- gbm(y ~ a + b + c, data = Data, distribution = "gaussian")
par(mfrow = c(2,2))
plot(gbm.gaus, i.var = 1)
plot(gbm.gaus, i.var = 2)
plot(gbm.gaus, i.var = 3)

Not surprisingly, for variables a and b the partial dependence plots yield horizontal lines around the mean of a. What me puzzles is the plot for variable c. I get horizontal lines for the ranges c < 40 and c > 60 and the y-axis is restricted to values close to the mean of y. Since a and b are completely unrelated to y (and thus there variable importance in the model is 0), I expected that c would show partial dependence along its entire range instead of that sigmoid shape for a very restricted range of its values. I tried to find information in Friedman (2001) "Greedy function approximation: a gradient boosting machine" and in Hastie et al. (2011) "Elements of Statistical Learning", but my mathematical skills are too low to understand all the equations and formulae therein. Thus my question: What determines the shape of the partial dependence plot for variable c? (Please explain in words comprehensible to a non-mathematician!)

ADDED on 17th April 2014:

While waiting for a response, I used the same example data for an analysis with R-package randomForest. The partial dependence plots of randomForest resemble much more to what I expected from the gbm plots: the partial dependence of explanatory variables a and b vary randomly and closely around 50, while explanatory variable c shows partial dependence over its entire range (and over almost the entire range of y). What could be the reasons for these different shapes of the partial dependence plots in gbm and randomForest?

partial plots of gbm and randomForest

Here the modified code that compares the plots:

a <- runif(100, 1, 100)
b <- runif(100, 1, 100)
c <- 1:100 + rnorm(100, mean = 0, sd = 5)
y <- 1:100 + rnorm(100, mean = 0, sd = 5)
par(mfrow = c(2,2))
plot(y ~ a); plot(y ~ b); plot(y ~ c)
Data <- data.frame(matrix(c(y, a, b, c), ncol = 4))
names(Data) <- c("y", "a", "b", "c")

library(gbm)
gbm.gaus <- gbm(y ~ a + b + c, data = Data, distribution = "gaussian")

library(randomForest)
rf.model <- randomForest(y ~ a + b + c, data = Data)

x11(height = 8, width = 5)
par(mfrow = c(3,2))
par(oma = c(1,1,4,1))
plot(gbm.gaus, i.var = 1)
partialPlot(rf.model, Data[,2:4], x.var = "a")
plot(gbm.gaus, i.var = 2)
partialPlot(rf.model, Data[,2:4], x.var = "b")
plot(gbm.gaus, i.var = 3)
partialPlot(rf.model, Data[,2:4], x.var = "c")
title(main = "Boosted regression tree", outer = TRUE, adj = 0.15)
title(main = "Random forest", outer = TRUE, adj = 0.85)
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    $\begingroup$ You might want to actually tune the hyperparameters a touch. I'm not sure what the default number of trees is in gbm, but it might be so small it doesn't have time to learn a healthy curvature. $\endgroup$ – Shea Parkes Apr 17 '14 at 12:56
  • $\begingroup$ @Shea Parkes - You're right. The defaukt number of trees is 100 which was not enough to generate a good model. With 2000 trees the partial dependence plots of gbm and random forest are almost identical. $\endgroup$ – user7417 Aug 21 '14 at 11:59
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I spent some time writing my own "partial.function-plotter" before I realized it was already bundled in the R randomForest library.

[EDIT ...but then I spent a year making the CRAN package forestFloor, which is by my opinion significantly better than classical partial dependence plots]

Partial.function plot are great in instances as this simulation example you show here, where the explaining variable do not interact with other variables. If each explaining variable contribute additively to the target-Y by some unknown function, this method is great to show that estimated hidden function. I often see such flattening in the borders of partial functions.

Some reasons: randomForsest has an argument called 'nodesize=5' which means no tree will subdivide a group of 5 members or less. Therefore each tree cannot distinguish with further precision. Bagging/bootstrapping layer of ensemple smooths by voting the many step functions of the individual trees - but only in the middle of the data region. Nearing the borders of data represented space, the 'amplitude' of the partial.function will fall. Setting nodesize=3 and/or get more observations compared to noise can reduce this border flatting effect... When signal to noise ratio falls in general in random forest the predictions scale condenses. Thus the predictions are not absolutely terms accurate, but only linearly correlated with target. You can see the a and b values as examples of and extremely low signal to noise ratio, and therefore these partial functions are very flat. It's a nice feature of random forest that you already from the range of predictions of training set can guess how well the model is performing. OOB.predictions is great also..

flattening of partial plot in regions with no data is reasonable: As random forest and CART are data driven modeling, I personally like the concept that these models do not extrapolate. Thus prediction of c=500 or c=1100 is the exactly same as c=100 or in most instances also c=98.

Here is a code example with the border flattening is reduced:

I have not tried the gbm package...

here is some illustrative code based on your eaxample...

#more observations are created...
a <- runif(5000, 1, 100)
b <- runif(5000, 1, 100)
c <- (1:5000)/50 + rnorm(100, mean = 0, sd = 0.1)
y <- (1:5000)/50 + rnorm(100, mean = 0, sd = 0.1)
par(mfrow = c(1,3))
plot(y ~ a); plot(y ~ b); plot(y ~ c)
Data <- data.frame(matrix(c(y, a, b, c), ncol = 4))
names(Data) <- c("y", "a", "b", "c")
library(randomForest)
#smaller nodesize "not as important" when there number of observartion is increased
#more tress can smooth flattening so boundery regions have best possible signal to             noise, data specific how many needed

plot.partial = function() {
partialPlot(rf.model, Data[,2:4], x.var = "a",xlim=c(1,100),ylim=c(1,100))
partialPlot(rf.model, Data[,2:4], x.var = "b",xlim=c(1,100),ylim=c(1,100))
partialPlot(rf.model, Data[,2:4], x.var = "c",xlim=c(1,100),ylim=c(1,100))
}

#worst case! : with 100 samples from Data and nodesize=30
rf.model <- randomForest(y ~ a + b + c, data = Data[sample(5000,100),],nodesize=30)
plot.partial()

#reasonble settings for least partial flattening by few observations: 100 samples and nodesize=3 and ntrees=2000
#more tress can smooth flattening so boundery regions have best possiblefidelity
rf.model <- randomForest(y ~ a + b + c, data = Data[sample(5000,100),],nodesize=5,ntress=2000)
plot.partial()

#more observations is great!
rf.model <- randomForest(y ~ a + b + c,
 data = Data[sample(5000,5000),],
 nodesize=5,ntress=2000)
plot.partial()
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As mentioned in the comments above, the gbm model would be better with some parameter tuning. An easy way to spot problems in the model and the need for such parameters is to generate some diagnostic plots. For example, for the gbm model above with the default parameters (and using the plotmo package to create the plots) we have

gbm.gaus <- gbm(y~., data = Data, dist = "gaussian")
library(plotmo)   # for the plotres function
plotres(gbm.gaus) # plot the error per ntrees and the residuals

which gives

plot

In the left hand plot we see that the error curve hasn't bottomed out. And in the right hand plot the residuals are not what we would want.

If we rebuild the model with a bigger number of trees

gbm.gaus1 <- gbm(y~., data = Data, dist = "gaussian",
                 n.trees=5000, interact=3)
plotres(gbm.gaus1)

we get

plot

We see the error curve bottom out with a large number of trees, and the residuals plot is healthier. We can also plot the partial dependence plots for the new gbm model and the random forest model

library(plotmo)
plotmo(gbm.gaus1, pmethod="partdep", all1=TRUE, all2=TRUE)
plotmo(rf.model,  pmethod="partdep", all1=TRUE, all2=TRUE)

which gives

plot

The gbm and random forest model plots are now similar, as expected.

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You need to update your interaction.depth parameter when you build your boosted model. It defaults to 1 and that will cause all the trees that the gbm algorithm builds to split only once each. This would mean every tree is just splitting on variable c and depending on the sample of observations it uses it will split somewhere around 40 - 60.

Here are the partial plots with interaction.depth = 3

enter image description here

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