随机森林填补缺失值具体代码为：

 def fill_missing(data,to_fill): 
    '''
    data:传递的数据集
    to_fill:需要填补的列名'''

    df = data.copy()  # 复制原表，防止操作错误
    columns = [*train_data.columns]  #特征
    columns.remove(to_fill)
    columns.remove('NumberOfDependents')
    columns.remove('SeriousDlqin2yrs')
    
# 提取特征和预测标签
    X = df.loc[:,columns]
    Y = df.loc[:,to_fill]
    # 划分数据集：训练集是没有缺失值的样本，测试集是缺失的样本
    Xtrain = X.loc[df[to_fill].notnull()]
    Xpred = X.loc[df[to_fill].isnull()]
    Ytrain = Y.loc[df[to_fill].notnull()]
    
# 建模
    model = RandomForestRegressor(random_state=0,
                                  n_estimators=100,
                                  max_depth=3,
                                 n_jobs=-1)
    model.fit(Xtrain,Ytrain)
   
#预测 
    pred = model.predict(Xpred).round()

#填充
    df.loc[df[to_fill].isnull(),to_fill]=pred
    return df


 train_data = fill_missing(train_data,'MonthlyIncome')