吴恩达的机器学习编程作业8：oneVsAll 多分类问题

最新推荐文章于 2024-03-21 17:11:52 发布

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最新推荐文章于 2024-03-21 17:11:52 发布 · 2k 阅读

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该博客详细介绍了如何使用oneVsAll方法来解决多分类问题，通过训练num_labels个逻辑回归分类器。文章中提供了一个函数`oneVsAll`，该函数将数据X和对应的标签y作为输入，并使用正则化参数lambda，返回一个包含所有分类器参数的矩阵。在代码中，使用fmincg优化器迭代地为每个类别训练模型，最终将训练好的模型参数存储在all_theta矩阵中。

function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta 
%corresponds to the classifier for label i
%   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
%   logistic regression classifiers and returns each of these classifiers
%   in a matrix all_theta, where the i-th row of all_theta corresponds 
%   to the classifier for label i

% Some useful variables
m = size(X, 1);
n = size(X, 2);

% You need to return the following variables correctly 
all_theta = zeros(num_labels, n + 1);

% Add ones to the X data matrix
X = [ones(m, 1) X];

% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the following code to train num_labels
%               logistic regression classifiers with regularization
%               parameter lambda. 
%
% Hint: theta(:) w