They are not known in the present when making the prediction. Boat and body are only known in the future after the event has already occurred. When using the original dataset, Information about the target label crept into the training data. You do not need a fancy machine learning algorithm to tell you that. ![]() Well, of course! If there is a body number, the passenger is dead. In the aftermath of the shipwreck, passengers were assigned a boat number if they safely made it to a lifeboat, or a body number if they were eventually found dead. The original data had additional features, two of which were particularly problematic: Boat and Body fields. What many do not know is, that the data used in the Kaggle challenge is the filtered, cleaned up version. Machine learning is used to learn such signals and predict which passengers survived the tragedy. Specific groups of passengers such as women, children and the upper class were more likely to survive than others. The lack of sufficient lifeboats was responsible for many lost lives in the aftermath of the shipwreck. In the machine learning community, the Titanic passenger survivability prediction is pretty well known. It is the accidental presence of information in the training data that will never legitimately be available in production, causing unrealistic results in the research environment while poor results in the production environment.Īlbert Einstein said: “If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.” So let's uncover the problem a bit more, with an example: Demystifying hindsight bias with Titanic The Achilles’ heel in this domain is Hindsight Bias (also known as label leakage or data leakage). In reality, there is often a lot of noise in the data. ![]() Machine learning algorithms often assume that a mythical “perfect dataset” is fed into them to predict the target label. This story repeats itself across different enterprise use cases, users and data. ![]() The business process built bias into the data from the ground up. If you train your machine learning algorithm with years of such labeled data, it will correlate those features with a positive label, though they would never really be available before the conversion. At the time of conversion, he filled in additional information for only those which had the positive outcome of conversion to purchases. Data entry is a pain, we all know that! As he worked through the process of converting the leads, some of them turned into purchases. Once upon a time, there was a sales executive, who tracked incoming sales leads by entering the minimal data needed to insert a lead record.
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