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I am investigating Elastic Net method on R to build a prediction model on pricing amount. I have about 70 dummies variables and results make sense regarding variable selection, stability...

However after looking at the observed vs predicted average values quantile by quantile, it looks like my prediction is too "flat" and we don't catch the real trend (observed in black, prediction in red): enter image description here

I assume my output fit a gamma distribution whereas cv.glmnet() does not handle gamma distribution (only gaussian, poisson, multinomiale...).

Does everyone has ever faced this issue and find a way to keep the gamma trend in the prediction?

Technical details:

  • I use LASSO model
cv.glmnet(x,y (or log(y)), alpha = 1, family = "gaussian")
  • For each model (lasso, ridge, log(y) or y...)

I always have a very high intercept value compared to coefficient value like:

(Intercept)                      1.211001e+01

Var 10                          -5.147049e-02

Var 15                          -7.939834e-04

...

So I have the feeling the predicted values are just moving around the intercept constant value...

H2O Packages results: enter image description here

enter image description here

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    $\begingroup$ try h2o.glm function in h2o package which provides elastice net with gamma distribution. $\endgroup$ – Sixiang.Hu Mar 30 '18 at 7:13
  • $\begingroup$ Thanks for the advice! I've just tried to use h2o package. My DB was uploaded successfully but I got only null coefficients now... (look at the 2 screenshots) $\endgroup$ – Benoit Four Mar 30 '18 at 8:42
  • $\begingroup$ looks like it penalies beta too much. Is your selected alpha = 1? You can also try h2o.grid to search for best alpha. $\endgroup$ – Sixiang.Hu Mar 30 '18 at 11:02
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This is a general phenomenon in regression (not specifically for elastic net resp. LASSO penalties) and related to regression to the mean. The weaker the model, the more narrow the distribution of the fitted values. In the worst case, i.e. if the model has no predictive strength, the predicted values are all the same.

n <- 100
set.seed(2)
x <- runif(n)
y <- x + rnorm(n)

fit <- lm(y ~ x)
summary(fit) # R-squared 0.1564

par(pty = "s")
qqplot(y, fitted(fit), xlim = c(-2, 4), ylim = c(-2, 4))
abline(0, 1)

enter image description here

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  • $\begingroup$ OK, I hadn't heard about this effect. It makes sense especially on my dataset, I'll make further investigation about how to counterbalance this effect... $\endgroup$ – Benoit Four Apr 2 '18 at 1:42

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