If they don't matter, it shouldn't increase the error rate much, right?Based on those scores, the most important features are: scores indicate how much more attractive the partners were relative to the participants.
One possible reason we are not so good at judging our own attractiveness is that for majority of people it it in the eye of the beholder.
If you plot to standard deviations of ratings people received, the spread is pretty wide, especially for men.
Features were generated using normalization and other techniques - see using the Classification Learner app.
What's nice about a Random Forest is that it can show you the predictor importance estimates based on how error increases if you randomly change the value of particular predictors.
The data comes from a series of heterosexual speed dating experiements at Columbia University from 2002-2004.
In these experiments, you each met all of you opposite-sex participants for four minutes.
Let's compare the self-rating for attractivess to the average ratings participants received.
If you subtract the average ratings received from the self rating, you can see how much people overestimate their attractiveness.
This means some people who got very high match rate must have requested a second date with almost everyone they met and they got their favor returned.
Does that mean people who made fewer matches were more picky and didn't request another date as often as those who were more successful? match rate - if they correlate, then we should see a diagonal line!
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