利用决策树算法对Iris数据集构建决策树并可视化
时间: 2023-07-20 12:03:25 浏览: 57
好的,我可以为您提供一个示例代码。首先,需要安装scikit-learn库和graphviz库。
代码如下:
```python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import graphviz
# 加载数据集
iris = load_iris()
# 构建决策树模型
clf = DecisionTreeClassifier()
clf.fit(iris.data, iris.target)
# 可视化决策树
dot_data = export_graphviz(clf, out_file=None,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
special_characters=True)
graph = graphviz.Source(dot_data)
graph.render("iris") # 保存决策树图像
```
运行后会生成一个iris.pdf文件,即为决策树的可视化结果。
相关问题
用matlab编写一个ID3算法对iris数据集进行处理并可视化生成决策树
好的,以下是用MATLAB编写ID3算法对iris数据集进行处理并可视化生成决策树的代码:
```matlab
% 加载iris数据集
data = readtable('iris.csv');
% 将标签转为数字编码
label = categorical(data.Var5);
label = grp2idx(label);
data.Var5 = label;
% 将表格转为矩阵
data = table2array(data);
% 进行数据集的划分,分为训练集和测试集
[trainData, testData] = splitData(data, 0.8);
% 构建决策树
tree = createTree(trainData);
% 可视化决策树
view(tree);
% 测试决策树
accuracy = testTree(tree, testData);
disp("测试集准确率:" + accuracy);
% ID3算法实现
function [tree] = createTree(data)
% 计算信息熵
label = data(:, end);
entropy = calcEntropy(label);
% 如果信息熵为0,说明该数据集已经完全分类,不需要继续构建子树
if entropy == 0
tree = struct('attribute', -1, 'value', -1, 'leaf', true, 'class', label(1));
return;
end
% 计算每个属性的信息增益
[numSamples, numFeatures] = size(data);
maxGain = -1;
bestAttribute = -1;
for i = 1 : (numFeatures - 1)
[gain, values] = calcGain(data, i, entropy);
if gain > maxGain
maxGain = gain;
bestAttribute = i;
bestValues = values;
end
end
% 如果没有属性可以用于分类,则返回叶子节点
if bestAttribute == -1
tree = struct('attribute', -1, 'value', -1, 'leaf', true, 'class', mode(label));
return;
end
% 构建子树
tree = struct('attribute', bestAttribute, 'value', -1, 'leaf', false, 'class', -1);
for i = 1 : length(bestValues)
value = bestValues(i);
subset = data(data(:, bestAttribute) == value, :);
if isempty(subset)
subtree = struct('attribute', -1, 'value', -1, 'leaf', true, 'class', mode(label));
else
subtree = createTree(subset);
end
subtree.value = value;
tree.subtree(i) = subtree;
end
end
% 计算信息熵
function [entropy] = calcEntropy(label)
classes = unique(label);
numSamples = length(label);
entropy = 0;
for i = 1 : length(classes)
p = sum(label == classes(i)) / numSamples;
entropy = entropy - p * log2(p);
end
end
% 计算信息增益
function [gain, values] = calcGain(data, attribute, entropy)
values = unique(data(:, attribute));
numSamples = size(data, 1);
gain = entropy;
for i = 1 : length(values)
value = values(i);
subset = data(data(:, attribute) == value, :);
p = size(subset, 1) / numSamples;
gain = gain - p * calcEntropy(subset(:, end));
end
end
% 划分数据集
function [trainData, testData] = splitData(data, ratio)
numSamples = size(data, 1);
numTrain = round(numSamples * ratio);
indices = randperm(numSamples);
trainIndices = indices(1:numTrain);
testIndices = indices(numTrain+1:end);
trainData = data(trainIndices, :);
testData = data(testIndices, :);
end
% 测试决策树
function [accuracy] = testTree(tree, testData)
numSamples = size(testData, 1);
numCorrect = 0;
for i = 1 : numSamples
sample = testData(i, :);
predictedClass = classifySample(tree, sample);
actualClass = sample(end);
if predictedClass == actualClass
numCorrect = numCorrect + 1;
end
end
accuracy = numCorrect / numSamples;
end
% 对样本进行分类
function [predictedClass] = classifySample(tree, sample)
if tree.leaf
predictedClass = tree.class;
return;
end
attribute = tree.attribute;
value = sample(attribute);
for i = 1 : length(tree.subtree)
if tree.subtree(i).value == value
predictedClass = classifySample(tree.subtree(i), sample);
return;
end
end
predictedClass = -1;
end
```
需要注意的是,这里用到了一个名为`splitData`的函数来将数据集随机划分为训练集和测试集,代码如下:
```matlab
function [trainData, testData] = splitData(data, ratio)
numSamples = size(data, 1);
numTrain = round(numSamples * ratio);
indices = randperm(numSamples);
trainIndices = indices(1:numTrain);
testIndices = indices(numTrain+1:end);
trainData = data(trainIndices, :);
testData = data(testIndices, :);
end
```
最后,需要注意的是,MATLAB自带的`view`函数可以可视化分类树,但它只适用于分类树而不适用于回归树。如果您想可视化一个回归树,建议使用MATLAB的`regtree`函数。
利用相关决策树算法构建Iris数据集的决策树并图形化显示。输出测试集分类结果的正确率和召回率。并写出代码
好的,以下是利用Python中的Scikit-learn库构建Iris数据集的决策树并图形化显示的代码:
```python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, recall_score
# 加载Iris数据集
iris = load_iris()
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
# 构建决策树模型
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)
# 可视化决策树
plot_tree(clf, filled=True)
# 输出测试集分类结果的正确率和召回率
y_pred = clf.predict(X_test)
print("Accuracy score: ", accuracy_score(y_test, y_pred))
print("Recall score: ", recall_score(y_test, y_pred, average="weighted"))
```
代码中使用Scikit-learn库加载Iris数据集,利用train_test_split函数将数据集划分为训练集和测试集,然后构建决策树模型。用plot_tree函数将决策树可视化,可以更加直观地了解决策树的结构。最后用accuracy_score和recall_score函数计算测试集分类结果的正确率和召回率。