The "type=1" importance measure in RF compares the prediction error of
each tree on the OOB data with the prediction error of the same tree on the OOB
data with the values of one variable randomly shuffled. If the variable has no
predictive power, then the two should be very close, and there's 50% chance
that the difference is negative. If the variable is "important", then
shuffling the values should significantly degrade the prediction in the form of
increased MSE. The importance measure takes mean of the differences of all
these individual tree MSEs and then divide by the SD of these differences.
With that, I hope it's clear that only v2 and v4 in your example are
potentially "important".
Best,
Andy
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Johnathan Mercer
Sent: Monday, August 27, 2012 11:40 AM
To: r-help at stat.math.ethz.ch
Subject: [R] interpret the importance output?
> importance(rfor.pdp11_t25.comb1,type=1)
%IncMSE
v1 -0.28956401263
v2 1.92865561147
v3 -0.63443929130
v4 1.58949137047
v5 0.03190940065
I wasn't entirely confident with interpreting these results based on the
documentation.
Could you please interpret?
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