案例背景

模型搭建

# 1.提取特征变量和目标变量
X = df.drop(columns=收益) 
y = df[收益]  

# 2.划分训练集和测试集
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=123)

# 3. 模型训练和搭建
from lightgbm import LGBMRegressor
model = LGBMRegressor()
model.fit(X_train, y_train)

模型预测及评估

# 预测测试数据
y_pred = model.predict(X_test)
y_pred[0:5]

# 预测值和实际值对比
a = pd.DataFrame()  # 创建一个空DataFrame 
a[预测值] = list(y_pred)
a[实际值] = list(y_test)
a.head()

# 查看评分
model.score(X_test, y_test)

# 特征重要性
model.feature_importances_

模型参数调优 

# 参数调优
from sklearn.model_selection import GridSearchCV  # 网格搜索合适的超参数
parameters = {num_leaves: [15, 31, 62], n_estimators: [20, 30, 50, 70], learning_rate: [0.1, 0.2, 0.3, 0.4]}  # 指定分类器中参数的范围
model = LGBMRegressor()  # 构建模型
grid_search = GridSearchCV(model, parameters,scoring=r2,cv=5) # cv=5表示交叉验证5次，scoring=r2表示以R-squared作为模型评价准则
# 输出参数最优值
grid_search.fit(X_train, y_train)  # 传入数据
grid_search.best_params_  # 输出参数的最优值

# 重新搭建LightGBM回归模型
model = LGBMRegressor(num_leaves=31, n_estimators=50,learning_rate=0.3)
model.fit(X_train, y_train)

# 查看得分
model.score(X_test, y_test)