# #003A Logistic Regression – Cost Function Optimization - Master Data Science

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First, to train parameters $$w$$ and $$b$$ of a logistic regression model we need to define a cost function. Given a training set of $$m$$ training examples, we want to find parameters $$w$$ and $$b$$, so that $$\hat{y}$$ is as close to $$y$$ (ground truth). Here, we will use $$(i)$$ superscript to index different training examples. Henceforth, we will use loss (error) function $$\mathcal{L}$$ to measure how well our algorithm is doing. In logistic regression squared error loss function is not an optimal choice.

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Dec-26-2021, 19:35:46 GMT

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