sklearn机器学习模板
时间: 2023-09-03 11:08:12 浏览: 40
以下是一个简单的sklearn机器学习模板,包含了常见的数据预处理、模型训练和评估步骤:
```
# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
# 读取数据
data = pd.read_csv('data.csv')
# 数据预处理
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# 训练模型
model = LogisticRegression()
# model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# 预测并评估模型
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
其中,需要根据具体的问题选择合适的模型,并在代码中进行相应的修改。