用python写一个lightGBM机器学习回归预测模型

好的，下面是一个简单的 LightGBM 回归模型的 Python 代码示例：

首先需要安装 LightGBM：

!pip install lightgbm

然后加载数据集，这里使用的是 sklearn 自带的波士顿房价数据集：

from sklearn.datasets import load_boston

boston = load_boston()
X, y = boston['data'], boston['target']

接下来，我们将数据集划分为训练集和测试集：

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

然后导入 LightGBM 并训练模型：

import lightgbm as lgb

train_data = lgb.Dataset(X_train, label=y_train)

params = {
    'objective': 'regression',
    'metric': 'l2',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9
}

num_round = 100

model = lgb.train(params, train_data, num_round)

最后，用模型进行预测并计算均方误差：

import numpy as np
from sklearn.metrics import mean_squared_error

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)

print(f"MSE: {mse}")

完整代码如下：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
import numpy as np

# 加载数据集
boston = load_boston()
X, y = boston['data'], boston['target']

# 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)

# 训练模型
train_data = lgb.Dataset(X_train, label=y_train)

params = {
    'objective': 'regression',
    'metric': 'l2',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9
}

num_round = 100

model = lgb.train(params, train_data, num_round)

# 预测并计算均方误差
y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)

print(f"MSE: {mse}")