Sure, it wouldn't do any harm to move to relevant and well-sourced examples, or construct such demonstrations specifically for the class. This HN discussion alo

Sure, it wouldn't do any harm to move to relevant and well-sourced examples, or construct such demonstrations specifically for the class. This HN discussion alone has gathered several instances of classifiers overfitting or fitting to features that do not generalize outside the training set as well as it first may appear, e.g. the curious case of leopard sofa [1] linked by andreasvc [2] demonstrates more or less same problem as the tank parable (leopard detector performs quite well but in reality is not detecting the exact thing we'd expect). However, also consider that the tank parable has circulated in textbooks and undergraduate introductory lectures for several decades. The main lesson I learned from the story that after training a model, one should validate it to see if it does generalize to detecting tanks both during night and daytime. Is it really a surprise that it might be difficult find egregiously naive mistakes? [1] http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.ht... [2] https://news.ycombinator.com/item?id=15486441 https://news.ycombinator.com/item?id=15486441