are then mapped to binary values of and where represents malignant tumors and represents benign tumors thereforeour first data instance is malignant tumor whose mean radius is + now that we have our data loadedwe can work with our data to build our machine learning classifier step -organizing data into sets to evaluate how well classifier is performingyou should always test the model on unseen data thereforebefore building modelsplit your data into two partsa training set and test set you use the training set to train and evaluate the model during the development stage you then use the trained model to make predictions on the unseen test set this approach gives you sense of the model' performance and robustness fortunatelysklearn has function called train_test_split()which divides your data into these sets import the function and then use it to split the dataml tutorial from sklearn model_selection import train_test_split split our data traintesttrain_labelstest_labels train_test_split(featureslabels