随机森林

利用随机森林进行分类：

% Since TreeBagger uses randomness we will get different results each % time we run this.% This makes sure we get the same results every time we run the code.%rng default % Here we create some training data.% The rows< represent the samples or individuals.% The first two columns represent the individual's features.% The last column represents the class label (what we want to predict)trainData = [ ...    [6,  300,  1];    [3,  300,  0];    [8,  300,  1];    [11, 2000, 0];    [3,  100,  0];    [6,  1000, 0];    ]; features = trainData(:,(1:2))classLabels = trainData(:,3) % How many trees do you want in the forest? nTrees = 20; % Train the TreeBagger (Decision Forest).B = TreeBagger(nTrees,features,classLabels, 'Method', 'classification'); % Given a new individual WITH the features and WITHOUT the class label,% what should the class label be?newData1 = [7, 300]; % Use the trained Decision Forest.predChar1 = B.predict(newData1); % Predictions is a char though. We want it to be a number.predictedClass = str2double(predChar1)% predictedClass =%      1 % So we predict that for our new piece of data, we will have a class label of 1  % Okay let's try another piece of data.newData2 = [7, 1500]; predChar2 = B.predict(newData2);predictedClass2 = str2double(predChar2)% predictedClass2 =%      0 % It predicts that the new class label is a 0.

利用随机森林进行回归：

x=[1:1:30];y=x.^2;B= TreeBagger(100,x',y','Method','regression');x2=[1:0.5:40];y2=x2.^2;y3=zeros(size(x2));for i=1:size(x2,2)    y3(i)=B.predict(x2(i));endplot(x2,y2,'.r');hold on;plot(x2,y3,'.b');