随机森林预测混凝土抗压强度、孔隙率、渗透系数模型Python代码

以下是使用随机森林算法进行混凝土抗压强度、孔隙率、渗透系数模型预测的Python代码示例：

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

# 读取数据
data = pd.read_csv('concrete_data.csv')

# 划分训练集和测试集
X = data.drop(['抗压强度', '孔隙率', '渗透系数'], axis=1)
y_strength = data['抗压强度']
y_porosity = data['孔隙率']
y_permeability = data['渗透系数']
X_train, X_test, y_train_strength, y_test_strength, y_train_porosity, y_test_porosity, y_train_permeability, y_test_permeability = train_test_split(X, y_strength, y_porosity, y_permeability, test_size=0.2, random_state=42)

# 构建随机森林模型
rf_strength = RandomForestRegressor(n_estimators=100, random_state=42)
rf_porosity = RandomForestRegressor(n_estimators=100, random_state=42)
rf_permeability = RandomForestRegressor(n_estimators=100, random_state=42)

# 训练模型
rf_strength.fit(X_train, y_train_strength)
rf_porosity.fit(X_train, y_train_porosity)
rf_permeability.fit(X_train, y_train_permeability)

# 预测测试集结果
y_pred_strength = rf_strength.predict(X_test)
y_pred_porosity = rf_porosity.predict(X_test)
y_pred_permeability = rf_permeability.predict(X_test)

# 计算模型的表现
print('抗压强度模型R^2值：', rf_strength.score(X_test, y_test_strength))
print('孔隙率模型R^2值：', rf_porosity.score(X_test, y_test_porosity))
print('渗透系数模型R^2值：', rf_permeability.score(X_test, y_test_permeability))

其中，`concrete_data.csv`是包含混凝土抗压强度、孔隙率、渗透系数以及其他相关特征的数据集，`train_test_split`函数用于将数据集划分为训练集和测试集，`RandomForestRegressor`类用于构建随机森林模型，`fit`方法用于训练模型，`predict`方法用于预测结果，`score`方法用于计算模型的表现。