#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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