1001 |
1 |
Decision tree learning |
NP |
1001 |
2 |
uses |
VP |
1001 |
3 |
a decision tree |
NP |
1001 |
4 |
as |
PP |
1001 |
5 |
a predictive model |
NP |
1001 |
6 |
which |
NP |
1001 |
7 |
maps |
VP |
1001 |
8 |
observations |
NP |
1001 |
9 |
about |
PP |
1001 |
10 |
an item |
NP |
1001 |
11 |
to |
PP |
1001 |
12 |
conclusions |
NP |
1001 |
13 |
about |
PP |
1001 |
14 |
the items target value |
NP |
1001 |
15 |
. |
O |
1002 |
1 |
It |
NP |
1002 |
2 |
is |
VP |
1002 |
3 |
one |
NP |
1002 |
4 |
of |
PP |
1002 |
5 |
the predictive modelling approaches |
NP |
1002 |
6 |
used |
VP |
1002 |
7 |
in |
PP |
1002 |
8 |
statistics , data mining and machine learning |
NP |
1002 |
9 |
. |
O |
1003 |
1 |
Tree models |
NP |
1003 |
2 |
where |
ADVP |
1003 |
3 |
the target variable |
NP |
1003 |
4 |
can take |
VP |
1003 |
5 |
a finite set |
NP |
1003 |
6 |
of |
PP |
1003 |
7 |
values |
NP |
1003 |
8 |
are called |
VP |
1003 |
9 |
classification trees |
NP |
1003 |
10 |
. |
O |
... |
... |
... |
... |