How to optimize a logistic regression in MATLAB?
Octave/MATLAB’s fminunc is an optimization solver that finds the minimum of an unconstrained function. For logistic regression, you want to optimize the cost function J (θ) with parameters θ.
How to implement the logistic regression hypothesis in machine learning?
The Logistic Regression Hypothesis: Implement following function in sigmoid.m. For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0.
How to implement the cost function in logistic regression?
Implement the cost function and gradient for logistic regression. The code in costFunction.m to return the cost and gradient. function [J, grad] = costFunction (theta, X, y) % Initialize some useful values m = length (y); % number of training examples % Compute the cost of a particular choice of theta. % You should set J to the cost.
Why is a logistic regression classifier trained on a feature vector?
A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will appear nonlinear when drawn in our 2-dimensional plot. While the feature mapping allows us to build a more expressive classifier, it also more susceptible to overfitting.
Is there a maximum likelihood function in MATLAB?
Please tell me if I made something wrong or if I maximized the function in the wrong way; I haven’t found any code on the Internet, only theory about maximum likelihood function and built-in Matlab function for logistic regression. Know someone who can answer? Share a link to this question via email, Twitter, or Facebook.
How to build a machine learning logistic regression model?
To help make the decision, we have a dataset of test results on past microchips, from which we can build a logistic regression model. plotDecisionBoundary.m is used to generate a figure where the axes are the two exam scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with different markers.