机器学习预测混凝土抗冻性代码
时间: 2024-10-16 21:19:43 浏览: 4
在机器学习中,预测混凝土抗冻性的代码通常涉及数据预处理、特征工程、模型选择和训练等步骤。以下是一个简化的Python示例,使用scikit-learn库:
```python
# 导入所需的库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# 加载数据集 (假设数据集名为'data.csv')
data = pd.read_csv('data.csv')
# 数据预处理
X = data.drop('混凝土抗冻性', axis=1) # 假设目标变量是最后一列
y = data['混凝土抗冻性']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 特征缩放
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# 模型选择 - 使用线性回归作为简单示例
model = LinearRegression()
model.fit(X_train_scaled, y_train)
# 预测
y_pred = model.predict(X_test_scaled)
# 评估模型性能
mse = mean_squared_error(y_test, y_pred)
print(f"均方误差(MSE): {mse}")
#
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