I have a dataset X which contains probabilities returned for classification 4 different classification models, say M1, M2, M3, M4 those probabilities are use to feed a fourth model M4 and that model also returns probabilities between (0, 1)
The 4 models above try to measure the probability of a customer to fall in default, and each of them measure different characteristics of customers.
So mi table X looks like:
M1 | M2| M3| M4 | y .5 | .7|.3 | .6 | 1 .3 | .6|.2 | .7 | 0 .7 | .1|.2 | .1 | 1 .1 | .2|.4 | .1 | 0 .6 | .7|.1 | .6 | 1 .2 | .9| 0 | .5 | 0
I have another binary variable
y for which I want to measure the impact of every model on
y so I want to find a measure of impact /influence of each model on this variable
What I tried so far was to run a logistic regresion on
X to explain
say I obtaine the coeffients:
Intercept : -3.74587192
b1 : 4.647923
b2 : -0.354599
b3 : 2.984094
b4 : 8.983295
Q1: Haw can I find a measure of influence on
y based on the Logistic regression coefficients?
Q2: Is it possible to establish a relationship of relative importance of each feature based on the coefficients?