{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":96164,"databundleVersionId":11418275,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"Hi everyone,\n\nFirst, I want to say this has been a fascinating competition. The creativity and intelligence on display in the notebooks and discussions are truly what makes Kaggle great.\n\nLately, I've noticed some submissions on the leaderboard with scores that are not just high, but in a league of their own—a significant gap above the rest. It's genuinely breathtaking.\n\nThis has led me to two possible conclusions, and I'd like to offer my sincere thoughts on both.\n\nScenario A: If these incredible scores are the result of groundbreaking modeling techniques, brilliant feature engineering, and a deep, intuitive understanding of the data—all while respecting the temporal split of the data—then I have nothing but the utmost admiration. You are pushing the boundaries of what's possible. Sharing even a small part of your methodology would be an immense contribution to the entire community, elevating everyone's skills and understanding. We would all be in your debt.\n\nScenario B: On the other hand, if these scores are achieved by leveraging information that wouldn't be available in a real-world predictive scenario (let's call it \"future data\")... then the nature of this achievement is fundamentally different. This is no longer modeling in the traditional sense, but rather a kind of \"sixth sense\" insight that transcends conventional data science. It suggests that, rather than struggling with models, some are more adept at finding \"shortcuts\" within the rules. This \"alternative path\" of wisdom is, frankly, eye-opening.\n\nAfter all, when a person can already \"see the future,\" asking them to come back and tune parameters or engineer features with us mortals is indeed doing them an injustice. Perhaps it's time to choose the blue pill, return to a simple happiness, and leave the mundane affair of \"modeling\" to us. This isn't giving up; it's a graceful retirement after a great success.\n\nFrom this perspective, I must truly thank you for your generous \"sharing.\" Your approach has given me a great revelation: the real key to a predictive model may not lie within the model itself. Inspired by this, I have successfully built a \"model\" that can accurately predict the next lottery numbers. When I claim my prize, I will be sure to give you half, as thanks for your mentorship.\n\nUltimately, the goal of these competitions is to build robust models that generalize to unseen data. I'm just putting some thoughts out there for discussion. What does everyone else think?\n\nCheers.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null}]}