model.feature_importances_ don't change ...

[psaks]

It doesn't seem that feature importances change. Using "lightgbm==2.3.0" I get the following;

`xval, yval = make_classification(n_samples = 1000, n_features=10)
model = lgb.LGBMClassifier(n_estimators=100, learning_rate = 0.05, verbose = -1)

for i in range(10):
model.fit(xval, yval)
print(model.feature_importances_)
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]
[244 537 213 214 183 222 282 264 175 648]`

If this is "correct" LightGBM behaviour, then there is obviously no need to average the feature_importances_ over multiple iterations.