# Effect of feature re-weighting on logistic regression

I've been recently working on the following problem:

Let $$F = \{F_1, F_2,F_3\}$$ denote a set of feature sets. For example, $$F_1$$ is comprised of 100 actual features. Before training a logistic regression classifier, I re-weight the individual feature sets (re-scaled between 0 and 1) as follows:

$$F_x = w_x * F_x,$$

i.e., by multiplying each feature set with a weight between 0 and 1.

My question is the following:

Assuming I get (for the aforementioned example) weights: $$\mathcal{W} = \{w_1 = 0.2, w_2 = 0.9,w_3 = 0.000004\}$$ (the index corresponds to an individual feature set from $$F$$).

Can I interpret this in the lines of: "The second feature set (2) contributes the most to the learning" etc.?

With other words: How sensitive is logistic regression to such changes in feature values.

Thanks!

Further, there is the trivial case when a given $$w = 0$$, as here no signal is present.