MATLAB – TreeBagger example

In MATLAB, Decision Forests go under the rather deceiving name of TreeBagger.

随机森林分类器（Random Forest）

B = TreeBagger(nTree,train_data,train_label，'Method','classification');
predict_label = predict(B,test_data);

利用随机森林做分类

Here’s a quick tutorial on how to do classification with the TreeBagger class in MATLAB.

% 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&lt; 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.

Found out how to inspect the trees, by running the view() command. E.g. for inspecting the first tree of the example:

view(B.Trees{1})Decision tree for classification
1  if x2<650 then node 2 elseif x2>=650 then node 3 else 0
2  if x1<4.5 then node 4 elseif x1>=4.5 then node 5 else 1
3  class = 0
4  class = 0
5  class = 1

By passing some more arguments to the view() command, the tree can also be visualized:

view(B.Trees{1},'mode','graph')

这里写图片描述

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));  
end  
plot(x2,y2,'.r');  
hold on;  
plot(x2,y3,'.b');  
title('Random Forest for Regression');