Feature Engineering - correlation with binary outcome - Titanic Dataset - Ticket feature
The thing is that you are looking at correlation with binary outcome. There as some counter intuitive things when doing that (see for example: https://stats.stackexchange.com/questions/103801/is-it-meaningful-to-calculate-pearson-or-spearman-correlation-between-two-boolea).
Outside than that, the features do not appears so much predictive.
More specifically for Is_group: Individual tickets (Is_group = 0), yields à 30% mean, while individuals with group tickets have around 50%. This is not really predictive.
Similarly, the average probability doesn't evolve monotonically of group size (from 0.3, to 0.6 then 0.7, then droping to 0.5 followed by 0 twice then back to 0.23). It doesn't seems really appropriate to use linear correlation to measure the relation between group_size and output here.