their definitions in the user guide when starting to work with mlpswe recommend sticking to 'adamand ' -bfgsfit resets model an important property of scikit-learn models is that calling fit will always reset everything model previously learned so if you build model on one datasetand then call fit again on different datasetthe model will "forgeteverything it learned from the first dataset you can call fit as often as you like on modeland the outcome will be the same as calling fit on "newmodel uncertainty estimates from classifiers another useful part of the scikit-learn interface that we haven' talked about yet is the ability of classifiers to provide uncertainty estimates of predictions oftenyou are not only interested in which class classifier predicts for certain test pointbut also how certain it is that this is the right class in practicedifferent kinds of mistakes lead to very different outcomes in real-world applications imagine medical application testing for cancer making false positive prediction might lead to patient undergoing additional testswhile false negative prediction might lead to serious disease not being treated we will go into this topic in more detail in there are two different functions in scikit-learn that can be used to obtain uncertainty estimates from classifiersdecision_function and predict_proba most (but not allclassifiers have at least one of themand many classifiers have both let' look at what these two functions do on synthetic two-dimensional datasetwhen building gradientboostingclassifier classifierwhich has both decision_function and predict_proba methodin[ ]from sklearn ensemble import gradientboostingclassifier from sklearn datasets import make_blobsmake_circles xy make_circles(noise= factor= random_state= we rename the classes "blueand "redfor illustration purposes y_named np array(["blue""red"])[ywe can call train_test_split with arbitrarily many arraysall will be split in consistent manner x_trainx_testy_train_namedy_test_namedy_trainy_test train_test_split(xy_namedyrandom_state= build the gradient boosting model gbrt gradientboostingclassifier(random_state= gbrt fit(x_trainy_train_nameduncertainty estimates from classifiers