然后构建并训练决策树分类模型,利用决策树分类模型预测客户的流失量,最后对决策树分类模型进行评价
时间: 2023-08-31 09:22:40 浏览: 50
好的,以下是根据提供的实验思路,构建并训练决策树分类模型,预测客户的流失量,并对模型进行评价的代码:
```python
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# 读取数据
data = pd.read_csv('customer_churn.csv')
# 提取特征
X = data[['IP', 'Duration', 'Credit', 'TalkTime']]
y = data['Churn']
# 划分训练数据和测试数据
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建决策树分类器
clf = DecisionTreeClassifier()
# 模型训练
clf.fit(X_train, y_train)
# 模型预测
y_pred = clf.predict(X_test)
# 计算模型评价指标
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))
print('Precision: {:.2f}'.format(precision))
print('Recall: {:.2f}'.format(recall))
print('F1 Score: {:.2f}'.format(f1))
```
需要注意的是,上述代码中的`customer_churn.csv`是包含IP地址、在网时长、信用等级、通话时长和客户流失情况的数据集。在实际应用中,我们需要根据具体情况选择合适的特征和数据集。同时,模型评价指标的选择也需要根据具体应用场景进行选择。