决策树算法python实现
时间: 2023-11-13 15:09:59 浏览: 49
决策树(Decision Tree)算法是一种基本的分类和回归算法,在机器学习中应用广泛。下面是使用Python实现决策树算法的示例代码:
1. 导入需要的库和数据集
```python
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
# 读取数据集
data = pd.read_csv("iris.csv")
```
2. 数据预处理
```python
# 将特征和标签分开
X = data.drop('species', axis=1)
y = data['species']
# 将标签转换为数字
y = pd.factorize(y)[0]
```
3. 划分训练集和测试集
```python
# 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=1)
```
4. 训练模型
```python
# 训练模型
clf = DecisionTreeClassifier()
clf = clf.fit(X_train,y_train)
```
5. 预测并评估模型
```python
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
```
完整代码如下:
```python
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
# 读取数据集
data = pd.read_csv("iris.csv")
# 将特征和标签分开
X = data.drop('species', axis=1)
y = data['species']
# 将标签转换为数字
y = pd.factorize(y)[0]
# 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=1)
# 训练模型
clf = DecisionTreeClassifier()
clf = clf.fit(X_train,y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
```
以上就是使用Python实现决策树算法的示例代码,希望能对您有所帮助。
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