## 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.