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AI: a super general term, means a bit of everything... and nothing at the same time. It's all about building intelligent machines, even though its meaning is not fully developed. It's not used in a rigorous way at all. In fact, scientists prefer to use more technical terms like the others you listed. ML: it's an approach to data, and it's all based on ...


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Artificial Intelligence It's a general idea to attain human-level intelligence using machines. This is more of a pursuit of human-kind i.e. an ongoing journey. It goes back to 1950 when Alan Turing devised the Idea of the Turing test. Machine Learning This is an approach where the program(model) is automatically built using the data. This approach is unlike ...


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Machine learning is the field that researches methods that fit (= optimize) model structures to data and output a final model that is based on the combination of model structure and optimized model parameters. Statistics and ML are intersecting fields, but some methods belong only to ML and some only to statistics. Neural networks are a set of such machine ...


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Machine Learning (ML) is a subset of Artificial Intelligence (AI). For example, the Minimax algorithm is also part of the larger field of AI, but the approach is not based on ML. In fact these algorithms showed much more promising results in the earlier stages of AI research (Deep Blue was the first computer to win a grandmaster in chess and it did not use ...


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It looks to me like what you propose makes sense, but there has been some research done around these questions of time representation already. I'd suggest you check the state of the art in this domain, if only not to reinvent the wheel or miss important cases. I'm not very knowledgeable about it but I can at least point you to TimeML and the related ...


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Taken from this question on Github and if you are using a tree-based classifier like XGBoost: This is because the XGBoost Tree SHAP algorithm computes the SHAP values with respect to the margin not the transformed probability. So the values you are seeing are log odds values (what XGBoost would output if pred_margin=True were set). This means, the values ...


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