Unlike supervised learning, unsupervised learning is used as part of exploratory data analysis (EDA) which aims to depict the main characteristics of a data set. However, it is difficult to use cross-validation in order to check the validity of our results when using unsupervised learning methods. This is because unlike supervised learning, which can check the accuracy of a predictive model by using it on data not used in fitting the model, unsupervised learning does not have any output values to match to, therefore making it difficult to assess the results since we don’t know the ‘true’ answer.