用python实现三个数据集的决策树模型
时间: 2024-10-13 10:16:30 浏览: 11
在Python中,我们可以使用sklearn库来创建决策树模型,包括对多个数据集的处理。以下是一个简单的步骤,演示如何为三个不同的数据集(例如Iris、Breast Cancer和Diabetes数据集)建立决策树模型:
首先,确保已经安装了必要的库,如pandas和sklearn。如果还没有安装,可以使用pip进行安装:
```bash
pip install pandas scikit-learn
```
然后,导入所需的模块:
```python
import pandas as pd
from sklearn.datasets import load_iris, load_breast_cancer, load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
```
针对每个数据集,我们分步操作:
**1. 加载数据集**
```python
# 加载数据
iris = load_iris()
bcancer = load_breast_cancer()
diabetes = load_diabetes()
# 将数据转换为DataFrame,方便分析
iris_df = pd.DataFrame(data= iris.data, columns= iris.feature_names)
bcancer_df = pd.DataFrame(data= bcancer.data, columns= bcancer.feature_names)
diabetes_df = pd.DataFrame(data= diabetes.data, columns= diabetes.feature_names)
# 将标签作为目标变量
iris_target = iris.target
bcancer_target = bcancer.target
diabetes_target = diabetes.target
```
**2. 划分训练集和测试集**
```python
# 使用80%的数据作为训练集,剩余20%作为测试集
X_train, X_test, y_train, y_test = train_test_split(iris_df, iris_target, test_size=0.2, random_state=42)
X_train_bcancer, X_test_bcancer, y_train_bcancer, y_test_bcancer = train_test_split(bcancer_df, bcancer_target, test_size=0.2, random_state=42)
X_train_diabetes, X_test_diabetes, y_train_diabetes, y_test_diabetes = train_test_split(diabetes_df, diabetes_target, test_size=0.2, random_state=42)
```
**3. 创建并训练决策树模型**
```python
# 对于每个数据集
for data_name, (X, y) in zip(['Iris', 'Breast Cancer', 'Diabetes'], [(X_train, y_train), (X_train_bcancer, y_train_bcancer), (X_train_diabetes, y_train_diabetes)]):
tree_model = DecisionTreeClassifier(random_state=42)
tree_model.fit(X, y)
# 预测测试集
predictions = tree_model.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, predictions)
print(f"{data_name} 数据集的决策树模型准确性: {accuracy * 100}%")
```
这将分别针对每个数据集训练一个决策树模型,并计算其在测试集上的准确度。
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