![]() It takes a linear combination of features and applies to them a nonlinear sigmoidal function. Its most significant advantage is that it can be used both for classification and class probability estimation, because it is tied with logistic data distribution. Logistic regression is another fundamental method initially formulated by David Cox in 1958 32 that builds a logistic model (also known as the logit model). The plots below show how the cost relates to our prediction (the first plot depicts how cost changes relative to our prediction when the Actual Outcome =1 and the second plot shows the same but when the A ctual Outcome = 0).Aleksandra Bartosik, Hannes Whittingham, in The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry, 2021 Logistic regression The model was pretty sure that I would miss and it was wrong so we want to strongly penalize it we are able to do so thanks to taking the natural log. ![]() In this case we are massively wrong and our cost would be: ![]()
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