Question

I am working on a logistic regression model in python where I am using NBA data to predict whether a player is a good rebounder or not based on various predictor variables. And my reg.coef is = array([[-1.50137324, -1.77554507, -1.73097902, 0.2568646 , 0.73556433,
0.00773832, -0.30204417, 0.26622955, 0.21256178]])

interpret the coefficients of your logistic regression model shown above

Answer 1

Now there are two cases:

case_1: If you did normalization/reshape your data then it can be said that the feature 4(highest magnitude coefficient) is the most significant one and for binary classification and has a positive correlation with the rebounding property of the player.

case_2: If you didn't normalize/reshape your data then there are two possibilities:

sucase_1: If the data is very similar in magnitude, a larger positive or negative coefficient means a larger effect, all things being equal.

subcase_2: If the data isn't very similar in magnitude, then the magnitude of the coefficients is without context i.e. they are not meaningful at all. For instance, you could get different coefficients by changing the units of measure to be larger or smaller.