Data Science, Machine Learning, Statistics, Open Source, Crossvalidation.
methodologically incorrect: we shouldn’t train and evaluate with the same data since our model already saw the training examples. Hence, it could simply memorize the labels and have perfect accuracy. We split our data into two parts: training and testing to simulate production conditions