使用jupyter实现一份鸢尾花数据的决策树分类
时间: 2023-12-24 21:02:36 浏览: 19
好的,以下是使用 Jupyter 实现鸢尾花数据的决策树分类的步骤:
1. 导入必要的库:
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
```
2. 加载数据集:
```python
iris = load_iris()
X = iris.data
y = iris.target
```
3. 将数据集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=0.3, random_state=1)
```
4. 定义决策树模型:
```python
dtc = DecisionTreeClassifier()
```
5. 使用训练集对模型进行训练:
```python
dtc.fit(X_train, y_train)
```
6. 使用测试集对模型进行预测:
```python
y_pred = dtc.predict(X_test)
```
7. 计算模型的准确率:
```python
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
```
完整代码如下:
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
# 定义决策树模型
dtc = DecisionTreeClassifier()
# 使用训练集对模型进行训练
dtc.fit(X_train, y_train)
# 使用测试集对模型进行预测
y_pred = dtc.predict(X_test)
# 计算模型的准确率
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
```
运行代码后,即可得出模型的准确率。