Stanford 机器学习 Week6 作业:Regularized Linear Regression and Bias v.s. Variance

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linearRegCostfunction

m = length(y); J = 0;grad = zeros(size(theta));J = 1.0 / 2 / m * ( sum( (X * theta - y) .^ 2) + lambda * sum(theta(2:end) .^2) );grad = 1 / m * ((X * theta - y)' * X)';grad(2:end) = grad(2:end) + theta(2:end) * lambda / m ;

m = size(X, 1);for i  = 1:m    theta = trainLinearReg(X(1:i,:),y(1:i),lambda);    error_train(i) = linearRegCostFunction(X(1:i,:),y(1:i),theta,0);    error_val(i) = linearRegCostFunction(Xval,yval,theta,0);end

validationCurve

lambda_vec = [0 0.001 0.003 0.01 0.03 0.1 0.3 1 3 10]';error_train = zeros(length(lambda_vec), 1);error_val = zeros(length(lambda_vec), 1);m = length(lambda_vec);for i = 1:m    theta = trainLinearReg(X, y, lambda_vec(i));    error_train(i) = linearRegCostFunction(X, y, theta, 0);    error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);end

注意算error的时候都是不带regularization的

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