["python","machine-learning","scikit-learn","logistic-regression","feature-selection"]
How to find the importance of the features for a logistic regression model?

I have a binary prediction model trained by logistic regression algorithm. I want know which features (predictors) are more important for the decision of positive or negative class. I know there is coef_ parameter which comes from the scikit-learn package, but I don't know whether it is enough for the importance. Another thing is how I can evaluate the coef_ values in terms of the importance for negative and positive classes. I also read about standardized regression coefficients and I don't know what it is. Lets say there are features like size of tumor, weight of tumor, and etc to make a decision for a test case like malignant or not malignant. I want to know which of the features are more important for malignant and not malignant prediction.
One of the simplest options to get a feeling for the "influence" of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data. Consider this example: import numpy as np from sklearn.linear_model import LogisticRegression x1 = np.random.randn(100) x2 = 4*np.random.randn(100) x3 = 0.5*np.random.randn(100) y = (3 + x1 + x2 + x3 + 0.2*np.random.randn()) > 0 X = np.column_stack([x1, x2, x3]) m = LogisticRegression() m.fit(X, y) # The estimated coefficients will all be around 1: print(m.coef_) # Those values, however, will show that the second parameter # is more influential print(np.array(np.std(X, 0))*m.coef_) An alternative way to get a similar result is to examine the coefficients of the model fit on standardized parameters: m.fit(X / np.std(X, 0), y) print(m.coef_) Note that this is the most basic approach and a number of other techniques for finding feature importance or parameter influence exist (using p-values, bootstrap scores, various "discriminative indices", etc). I am pretty sure you would get more interesting answers at https://stats.stackexchange.com/.