I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| user_id | age | car_price | car_age | income | education | gender | crashes | probability | true_labes |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| 1 | 29 | 15600 | 3 | 20000 | 3 | 1 | 1 | 0.23 | 0 |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| 2 | 41 | 43000 | 1 | 65000 | 2 | 0 | 1 | 0.1 | 0 |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| 3 | 39 | 23500 | 5 | 43000 | 3 | 1 | 0 | 0.46 | 1 |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| 4 | 19 | 12200 | 3 | 13000 | 1 | 1 | 0 | 0.34 | 1 |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
| 5 | 68 | 21900 | 2 | 31300 | 3 | 0 | 1 | 0.85 | 1 |
+---------+-----+-----------+---------+--------+-----------+--------+---------+-------------+------------+
You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).