# Random forest regression not predicting higher than training data

I've noticed that when building random forest regression models, at least in R, the predicted value never exceeds the maximum value of the target variable seen in the training data. As an example, see the code below. I'm building a regression model to predict mpg based on the mtcars data. I build OLS and random forest models, and use them to predict mpg for a hypothetical car that should have very good fuel economy. The OLS predicts a high mpg, as expected, but random forest does not. I've noticed this in more complex models too. Why is this?

> library(datasets)
> library(randomForest)
>
> data(mtcars)
> max(mtcars$mpg) [1] 33.9 > > set.seed(2) > fit1 <- lm(mpg~., data=mtcars) #OLS fit > fit2 <- randomForest(mpg~., data=mtcars) #random forest fit > > #Hypothetical car that should have very high mpg > hypCar <- data.frame(cyl=4, disp=50, hp=40, drat=5.5, wt=1, qsec=24, vs=1, am=1, gear=4, carb=1) > > predict(fit1, hypCar) #OLS predicts higher mpg than max(mtcars$mpg)
1
37.2441
> predict(fit2, hypCar) #RF does not predict higher mpg than max(mtcars$mpg) 1 30.78899  • Is it common that people refer to linear regressions as OLS? I've always thought of OLS as a method. Commented Sep 16, 2016 at 4:18 • I believe OLS is the default method of linear regression, at least in R. Commented Sep 16, 2016 at 4:27 • For random trees/forest, the predictions are the average of the training data in the corresponding node. So it cannot be bigger than the values in training data. Commented May 2, 2017 at 16:03 • I agree but it's been answered by at least three other users. Commented May 2, 2017 at 17:16 ## 3 Answers As it has been mentioned already in previous answers, random forest for regression / regression trees doesn't produce expected predictions for data points beyond the scope of training data range because they cannot extrapolate (well). A regression tree consists of a hierarchy of nodes, where each node specifies a test to be carried out on an attribute value and each leaf (terminal) node specifies a rule to calculate a predicted output. In your case the testing observation flow through the trees to leaf nodes stating, e.g., "if x > 335, then y = 15", which are then averaged by random forest. Here is an R script visualizing the situation with both random forest and linear regression. In random forest's case, predictions are constant for testing data points that are either below the lowest training data x-value or above the highest training data x-value. library(datasets) library(randomForest) library(ggplot2) library(ggthemes) # Import mtcars (Motor Trend Car Road Tests) dataset data(mtcars) # Define training data train_data = data.frame( x = mtcars$hp,  # Gross horsepower
y = mtcars$qsec) # 1/4 mile time # Train random forest model for regression random_forest <- randomForest(x = matrix(train_data$x),
y = matrix(train_data$y), ntree = 20) # Train linear regression model using ordinary least squares (OLS) estimator linear_regr <- lm(y ~ x, train_data) # Create testing data test_data = data.frame(x = seq(0, 400)) # Predict targets for testing data points test_data$y_predicted_rf <- predict(random_forest, matrix(test_data$x)) test_data$y_predicted_linreg <- predict(linear_regr, test_data)

# Visualize
ggplot2::ggplot() +
# Training data points
ggplot2::geom_point(data = train_data, size = 2,
ggplot2::aes(x = x, y = y, color = "Training data")) +
# Random forest predictions
ggplot2::geom_line(data = test_data, size = 2, alpha = 0.7,
ggplot2::aes(x = x, y = y_predicted_rf,
color = "Predicted with random forest")) +
# Linear regression predictions
ggplot2::geom_line(data = test_data, size = 2, alpha = 0.7,
ggplot2::aes(x = x, y = y_predicted_linreg,
color = "Predicted with linear regression")) +
# Hide legend title, change legend location and add axis labels
ggplot2::theme(legend.title = element_blank(),
legend.position = "bottom") + labs(y = "1/4 mile time",
x = "Gross horsepower") +
ggthemes::scale_colour_colorblind()


There's no way to a Random Forest to extrapolate like an OLS do. The reason is simple: the predictions from a Random Forest are done through averaging the results obtained in several trees. The trees themselves output the mean value of the samples in each terminal node, the leaves. It's impossible for the result to be outside the range of the training data, because the average is always inside the range of its constituents.

In other words, it's impossible for an average to be bigger (or lower) than every sample, and Random Forests regressions are based on averaging.

Decision Trees / Random Forrest cannot extrapolate outside of the training data. And although OLS can do this, such predictions should be looked at with caution; as the identified pattern may not continue outside of the observed range.