Gradient Descent: Logistic Regression

Logistic regression

Consider the scenario where we want to create a model that predicts whether an individual has diabetes or not. This is a case of binary classification and let’s assume that the input matrix $X = \begin{bmatrix} x_1^T \\ x_2^T \\ . \\. \\ . \\ x_N^T \end{bmatrix} \in \R^{N \times d}$ represents the collection of the $d$-dimensional input features, such as age, weight, height, cholesterol, etc., for $N$ patients. $Y=\left[\begin{array}{c}{y}_1\\ y_2\\ \mathrm{.}\\ \mathrm{.}\\ \mathrm{.}\\ y_N\end{array}\right]\in \{0,1{\}}^N$ ...