Regularization in Logistic Regression: Same Idea, Different Function
Exploring the language of data, one algorithm at a time. Machine learning enthusiast, AI researcher, and advocate for tech-driven solutions to real-world challenges. Passionate about using AI for public safety and ethical technology. Join me as I document my journey through data, models, and insights.
Well, still similar to the Regularization applied to linear, the regularized cost function for logistic regression is defined by:
$$J(\mathbf{w}, b) = -\frac{1}{m} \sum_{i=1}^m \left[ y^{(i)} \log(f_{\mathbf{w}, b}(\mathbf{x}^{(i)})) + (1 - y^{(i)}) \log(1 - f_{\mathbf{w}, b}(\mathbf{x}^{(i)})) \right] + \frac{\lambda}{2m} \sum_{j=1}^n w_j^2$$
For the gradient descent, it's similar to the linear regression. we just have to remember that the definition of f(x) is different for logistic.
Now, Onwards to learning Advanced algorithms. see you next time
