I'm analyzing the Titanic dataset, and I've been trying to understand the predictive power of the Age feature relative to passenger survival. My intuition is that younger people had a higher chance to survive because they were more resistant to cold water, or that older people survived because they were taken care of by younger ones.
While I can see no difference between the distributions of the two populations, survivors and victims:
the permutation importance for my XGBoost model suggests that the Age feature has a certain importance in predicting whether someone survived or not.
Is this difference coming from the fact that:
- The predictive power is so small it cannot be observed from a violin plot?
- Age alone is not enough to predict survival, but only in combination with other features?
- I am misinterpreting the violin plot?
I know it might be impossible to say without having the dataset on hand, so I'm curious what approaches I could try to use to find the reason behind this difference.
Summary plot suggests that:
- Higher age is associated with not surviving
- Population of younger people (lower age) is smaller than that of older people (there are a few blue spots on the right)