# ex.m er line by line explaination

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This code is for logistic regression, a type of binary classification algorithm. It uses a dataset with exam scores as features and admission status (admitted or not admitted) as the target variable. clear ; close all; clc clears all variables in the workspace, closes all figures, and clears command window. data = load('ex2data1.txt') loads data from a text file named 'ex2data1.txt' and assigns it to a variable called data. X = data(:, [1, 2]) creates a matrix X containing the first two columns of the data matrix. y = data(:, 3) creates a vector y containing the third column of the data matrix. plotData(X, y) displays a scatter plot of X with + indicating admitted students and o indicating non-admitted students. hold on holds the current plot so that subsequent plots are added to it rather than replacing it. xlabel('Exam 1 score') labels the x-axis of the plot. ylabel('Exam 2 score') labels the y-axis of the plot. legend('Admitted', 'Not admitted') adds a legend to the plot. hold off releases the current plot. options = optimset('GradObj', 'on', 'MaxIter', 400) sets options for the optimization function fminunc, which will be used to find the optimal parameters theta. [theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options) finds the optimal values of the parameters vector theta and the minimum cost cost using the fminunc function. The @(t)(costFunction(t, X, y)) defines the objective function that the optimizer should minimize. prob = sigmoid([1 45 85] * theta) predicts the probability that a student with exam scores of 45 and 85 will be admitted using the learned parameters theta. p = predict(theta, X) predicts admission status for all students in the dataset using the learned parameters theta. mean(double(p == y)) * 100 calculates the accuracy of the model on the training set.