## What is logistic regression in machine learning?

Logistic regression is a classification approach for different classes of data in order to predict whether a data point belongs to one class or another. Sigmoid hypothesis function is used to calculate the probability of y belonging to a particular class.

## How do I make predictions using my learned logistic regression parameters?

function p = predict (theta, X) % Instructions: Complete the following code to make predictions using % your learned logistic regression parameters. % You should set p to a vector of 0’s and 1’s % h = sigmoid (X * theta); p = round (h); end

**How do you calculate sigmoid in logistic regression?**

function g = sigmoid (z) g = zeros (size (z)); # Compute the sigmoid of each value of z (z can be a matrix, vector or scalar). g = 1 ./ (1 + exp (-z)) end Implement the cost function and gradient for logistic regression.

**How to implement the cost function and gradient for 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.

### What is the pihat of the multinomial logistic regression?

pihat = mnrval (B,X) returns the predicted probabilities for the multinomial logistic regression model with predictors, X, and the coefficient estimates, B. pihat is an n -by- k matrix of predicted probabilities for each multinomial category.

### How to fit a multinomial regression model for categorical responses?

Fit a multinomial regression model for categorical responses with natural ordering among categories. Then estimate the upper and lower confidence bounds for the category probability estimates. Load the sample data and define the predictor variables.

**How do I make a linear regression model from carsmall data?**

Load the carsmall data set. Identify weight and horsepower as predictors and mileage as the response. Compute the regression coefficients for a linear model with an interaction term. Plot the data and the model.

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