It's a method often said to be included in a field of statistical computing (called machine learning, statistical learning, artificial intelligence, data mining, supervised learning, etc). Decision trees usefully split up datasets into groups, often using YES/NO questions at each split.
I'm using data from Qriously (date 2017-06-07) in the run-up to the UK general election. I'm looking only at England & Wales, and I've only considered 3 regressors: gender (0=F, 1=M), age, income. I've considered a YES/NO voting intention for the 5 biggest political parties.
The trees are below. Here are some key aspects that jump out:
- Age seems to be the most important regressor for most parties.
- CON seems to get many votes from older voters (except if they're poor).
- CON gets few votes from younger votes (especially poorer voters).
- CON's best group were older females (not males as one might expect - maybe this is simply a bias of longer life expectancy for females).
- LAB/CON results are fairly inverted (as we might expect), i.e. poorer and younger voters favouring LAB.
- LAB's best group were young, poor females.
- LAB's worst group are the 65+.
- LIB seems to do best from low- and middle-income voters, more-so for male voters.
- LIB's two worst groups are from (a) elderly richer females, and (b) poorer older voters.
- GRN's voters are generally younger (the one exception being wealthier older females) -- young males is one key group.
- GRN does badly with (a) older, poorer voters and (b) older, richer males.
- UKIP voters are generally poorer. One key group being poorer younger males.