For discriminant analysis, the score of
a classification is the posterior probability of the classification.
For the definition of posterior probability in discriminant analysis,
see Posterior Probability.
For ensembles, a classification score represents
the confidence of a classification into a class. The higher the score,
the higher the confidence.
Different ensemble algorithms have different definitions for
their scores. Furthermore, the range of scores depends on ensemble
type. For example:
For trees, the score of a classification
of a leaf node is the posterior probability of the classification
at that node. The posterior probability of the classification at a
node is the number of training sequences that lead to that node with
the classification, divided by the number of training sequences that
lead to that node.
For example, consider classifying a predictor X
as true
when X
< 0.15
or X
> 0.95
, and X
is
false otherwise.
Generate 100 random points and classify them:
Prune the tree:
The pruned tree correctly classifies observations that are less
than 0.15 as true
. It also correctly classifies
observations from .15 to .94 as false
. However,
it incorrectly classifies observations that are greater than .94 as false
.
Therefore, the score for observations that are greater than .15 should
be about .05/.85=.06 for true
, and about .8/.85=.94
for false
.
Compute the prediction scores for the first 10 rows of X
:
ans = 10×3
0.9059 0.0941 0.8147
0.9059 0.0941 0.9058
0 1.0000 0.1270
0.9059 0.0941 0.9134
0.9059 0.0941 0.6324
0 1.0000 0.0975
0.9059 0.0941 0.2785
0.9059 0.0941 0.5469
0.9059 0.0941 0.9575
0.9059 0.0941 0.9649
Indeed, every value of X
(the right-most
column) that is less than 0.15 has associated scores (the left and
center columns) of 0
and 1
,
while the other values of X
have associated scores
of 0.91
and 0.09
. The difference
(score 0.09
instead of the expected .06
)
is due to a statistical fluctuation: there are 8
observations
in X
in the range (.95,1)
instead
of the expected 5
observations.