How to interpret coefficients from logistic regression? I ran a logistic regression (statsmodel) on my data with 60 features using the below code

import statsmodels.api as sm
logit_model=sm.Logit(y_train,X_train_std)
result=logit_model.fit()
print(result.summary())


I was able to see that few variables had negative coefficient and few had positive coefficients.

Am I right to understand that irrespective of sign of coefficients, all the below variables are significant predictors that influence the outcome?

Or does negative coefficient mean they don't have any influence on the model outcome? But p-value is significant. Am a bit confused. Can you help in simple terms please

The below output shows the records whose p-values were less than 0.05
They are all significant but for certain thing.
What do I mean? You are predicting evidence, i.e. first column of the following picture:

In other words you have &quot;linear regression part&quot;+ instead of y you have evidence. So changing values of independnet variable X (positive or negative) will influence different binary class (0 or 1), hence different values are significant for different thing. (they add some info)