# self study: why is my neural network so much worse than my random forest

Playing around with machine learning in R. Say I have this arbitrary function:

set.seed(123)
n = 1e3
x = rnorm(n)
y = 1 + 3*sin(x/2) + 15*cos(pi*x) + rnorm(n = length(x))
df = data.frame(y,x)
# train/test
df$train = sample(c(TRUE, FALSE), length(y), replace=TRUE, prob=c(0.7,0.3)) df_train = subset(df, train = TRUE) df_test = subset(df, train = FALSE)  take a look: plot(df_train$x,df_train$y)  a random forest fits nicely: library(randomForest) model_rf = randomForest(y~x, data = df_train) yh = predict(m, newdata = df_test) plot(df_test$x,df_test$y) points(df_test$x,yh, col = 'orange')


but a neural net does terribly, regardless of how many layers I add (did 10 here):

library(nnet)
nn = nnet(y~x, data = df_train, size = 2)
yh2 = predict(nn, newdata = df_test)
plot(df_test$x,df_test$y)
points(df_test$x, yh2, col = 'blue')  Is this an artifact of the data? Or am I doing something blatantly wrong? • size=2 specifies 2 hidden units. Using more units would make the neural network more flexible. – Sycorax Jun 24, 2022 at 1:38 ## 1 Answer By default, nnet is doing classification. You want to set linout=T to make it do regression. Then, increase the number of hidden units as suggested by Sycorax, and/or increase the number of iterations n_its. For example: library(nnet) nn = nnet(y~x, data = df_train, size = 25, linout=T) yh2 = predict(nn, newdata = df_test) plot(df_test$$x,df_test$$y) points(df_test$x, yh2, col = 'blue')


• +1 Getting into the statistics, this is akin to fitting a logistic regression when a linear model is appropriate.
– Dave
Jun 24, 2022 at 3:00
• yeah good point Jun 24, 2022 at 5:31