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.