gbdt回归算法matlab代码
时间: 2023-09-15 12:21:16 浏览: 46
由于GBDT回归算法需要导入数据,因此无法提供完整的代码。以下是一些实现GBDT回归算法的MATLAB函数:
1. 计算残差
function [residuals] = computeResiduals(y, y_hat)
% 计算残差
residuals = y - y_hat;
2. 计算损失函数
function [loss] = computeLoss(residuals)
% 计算损失函数
loss = mean(residuals.^2);
3. 计算负梯度
function [negative_gradient] = computeNegativeGradient(residuals)
% 计算负梯度
negative_gradient = residuals;
4. 计算叶节点值
function [leaf_values] = computeLeafValues(residuals, tree_depth)
% 计算叶节点值
leaf_values = mean(residuals)*0.1*tree_depth;
5. 计算预测值
function [y_hat] = computePredictions(X, trees, leaf_values)
% 计算预测值
n_samples = size(X, 1);
y_hat = zeros(n_samples, 1);
for i = 1:n_samples
x = X(i, :);
tree = trees{i};
node = 1;
while (tree(node, 4) ~= 0)
if (x(tree(node, 1)) <= tree(node, 2))
node = tree(node, 4);
else
node = tree(node, 5);
end
end
y_hat(i) = leaf_values(node);
end
6. 训练GBDT模型
function [trees, leaf_values] = trainGBDT(X, y, n_trees, max_depth, learning_rate)
% 训练GBDT模型
[n_samples, n_features] = size(X);
trees = cell(n_trees, 1);
leaf_values = zeros(n_trees, 1);
y_hat = zeros(n_samples, 1);
for i = 1:n_trees
residuals = computeResiduals(y, y_hat);
loss = computeLoss(residuals);
fprintf('Tree %d: loss = %f\n', i, loss);
negative_gradient = computeNegativeGradient(residuals);
tree = trainDecisionTree(X, negative_gradient, max_depth);
trees{i} = tree;
leaf_values(i) = computeLeafValues(residuals, tree_depth(tree));
y_hat = y_hat + learning_rate*computePredictions(X, trees, leaf_values(i));
end
7. 预测
function [y_hat] = predictGBDT(X, trees, leaf_values)
% 预测
y_hat = computePredictions(X, trees, leaf_values);