基于岭回归实现空气质量预测的python代码

以下基于岭回归实现空气质量预测的Python代码：

import pandas as pd
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 读取数据
data = pd.read_csv('air_quality.csv')
# 提取特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 岭回归模型
ridge = Ridge(alpha=0.5)
ridge.fit(X_train, y_train)
# 预测
y_pred = ridge.predict(X_test)
# 评估
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print('RMSE:', rmse)

其中，'air_quality.csv'为数据文件名，需自行准备。数据格式为：第一列为PM2.5浓度，后面的列为特征，共计7个特征。alpha为岭回归系数，可调整。

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编写具体的基于岭回归实现空气质量预测的python代码

好的，以下是基于岭回归实现空气质量预测的Python代码示例：

# 导入必要的库
import pandas as pd
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

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

# 特征选择
features = ['PM10', 'PM2.5', 'NO2', 'SO2', 'O3']

# 划分特征和标签
X = data[features]
y = data['AQI']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 岭回归模型训练
ridge = Ridge(alpha=0.1)  # alpha为正则化参数
ridge.fit(X_train, y_train)

# 预测测试集结果
y_pred = ridge.predict(X_test)

# 模型评估
mse = mean_squared_error(y_test, y_pred)  # 均方误差
rmse = np.sqrt(mse)  # 均方根误差
r2 = ridge.score(X_test, y_test)  # 决定系数

# 输出模型评估结果
print('MSE:', mse)
print('RMSE:', rmse)
print('R^2:', r2)

其中，数据集文件为'air_quality.csv'，特征选择为['PM10', 'PM2.5', 'NO2', 'SO2', 'O3']，岭回归模型的正则化参数alpha设为0.1。

岭回归与lasoo回归python实现的全代码

当然可以！下面是岭回归（Ridge Regression）和Lasso回归（Lasso Regression）的Python实现代码：

岭回归：

from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np

# 生成示例数据
np.random.seed(0)
X = np.random.rand(100, 10)
y = np.random.rand(100)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建岭回归模型
ridge = Ridge(alpha=1.0)

# 拟合模型
ridge.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = ridge.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差：", mse)

Lasso回归：

from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np

# 生成示例数据
np.random.seed(0)
X = np.random.rand(100, 10)
y = np.random.rand(100)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建Lasso回归模型
lasso = Lasso(alpha=1.0)

# 拟合模型
lasso.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = lasso.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差：", mse)

以上是岭回归和Lasso回归的Python实现代码。你可以根据实际情况，修改数据和超参数，运行代码进行训练和预测。希望对你有帮助！