random_state= the function randomly splits the data using the test_size parameter in this examplewe now have test set (testthat represents of the original dataset the remaining data (trainthen makes up the training data we also have the respective labels for both the train/test variablesi train_labels and test_labels we can now move on to training our first model step -building and evaluating the model there are many models for machine learningand each model has its own strengths and weaknesses in this tutorialwe will focus on simple algorithm that usually performs well in binary classification tasksnamely naive bayes (nbfirstimport the gaussiannb module then initialize the model with the gaussiannb(functionthen train the model by fitting it to the data using gnb fit()ml tutorial from sklearn naive_bayes import gaussiannb initialize our classifier gnb gaussiannb(train our classifier