# Scikit-learn estimator not changing predictions when random_state variable changes

I am trying to compute prediction intervals for a classifier I trained in scikit-learn. Even after setting a new random_state parameter in my pipeline, this does not seem to change my results when refitting on the data. What can I do about this? This is a relevant snippet of the code I am using:

SEED_VALUE = 3

t_clf = Pipeline(steps=[('preprocessor', preprocessor),
('lgbm', LGBMClassifier(class_weight="balanced",random_state=SEED_VALUE, max_depth=20, min_child_samples=20, num_leaves=31))

])
states = [0,1,2,3]

///

for state in states:

train_temp = train.copy()
t_clf.set_params(lgbm__random_state=state)
t_clf.fit(train_temp, train_temp['label'])
t_clf.predict_proba(test)

# output from predict probability doesn't change with varying states


The same occurs when trying to change shuffle order.

I realized that when shuffling I did not set the replace parameter to True which prevented randomness from being inserted into the process.

SEED_VALUE = 3

t_clf = Pipeline(steps=[('preprocessor', preprocessor),
('lgbm', LGBMClassifier(class_weight="balanced",random_state=SEED_VALUE, max_depth=20, min_child_samples=20, num_leaves=31))

])
states = [0,1,2,3]

///

for state in states:

train_temp = train.copy().sample(frac=1, replace=True).reset_index(drop=True)
t_clf.fit(train_temp, train_temp['label'])
t_clf.predict_proba(test)

# output from predict probability should be different each run