用朴素贝叶斯、支持向量机、决策树对鸢尾花数据集分类
时间: 2023-12-28 21:23:20 浏览: 55
以下是使用朴素贝叶斯、支持向量机、决策树对鸢尾花数据集分类的Python代码:
1. 朴素贝叶斯分类器
```python
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 创建朴素贝叶斯分类器
gnb = GaussianNB()
# 训练模型
gnb.fit(X_train, y_train)
# 预测测试集
y_pred = gnb.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("朴素贝叶斯分类器的准确率为:", accuracy)
```
2. 支持向量机分类器
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 创建支持向量机分类器
svc = SVC(kernel='linear')
# 训练模型
svc.fit(X_train, y_train)
# 预测测试集
y_pred = svc.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("支持向量机分类器的准确率为:", accuracy)
```
3. 决策树分类器
```python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 创建决策树分类器
dtc = DecisionTreeClassifier()
# 训练模型
dtc.fit(X_train, y_train)
# 预测测试集
y_pred = dtc.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("决策树分类器的准确率为:", accuracy)
```