鸢尾花分类python贝叶斯代码
时间: 2023-11-12 20:58:01 浏览: 65
以下是鸢尾花分类的Python贝叶斯代码:
```python
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
# 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)
# 创建高斯朴素贝叶斯分类器
gnb = GaussianNB()
# 训练模型
gnb.fit(X_train, y_train)
# 预测测试集结果
y_pred = gnb.predict(X_test)
# 输出准确率
print("Accuracy:", gnb.score(X_test, y_test))
```
相关问题
鸢尾花python贝叶斯分类代码
是鸢尾花数据集的Python贝叶斯分类代码,使用的是高斯朴素贝叶斯分类器:
```python
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
# 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)
# 创建高斯朴素贝叶斯分类器
gnb = GaussianNB()
# 训练模型
gnb.fit(X_train, y_train)
# 预测测试集
y_pred = gnb.predict(X_test)
# 输出预测结果
print(y_pred)
```
鸢尾花贝叶斯分类python
要使用贝叶斯分类器对鸢尾花数据集进行分类,可以按照以下步骤进行操作:
1. 导入所需的库和数据集:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
iris = load_iris()
X = iris.data
y = iris.target
```
2. 将数据集划分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
3. 创建贝叶斯分类器模型,并进行训练:
```python
model = GaussianNB()
model.fit(X_train, y_train)
```
4. 进行预测:
```python
y_pred = model.predict(X_test)
```
5. 评估模型性能:
```python
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
这样,你就可以使用贝叶斯分类器对鸢尾花数据集进行分类了。希望这对你有帮助!如果有任何进一步的问题,请随时提问。