随机森林回归模型预测测试结果一样

随机森林回归模型预测测试集为什么预测结果一样?这是什么原因造成的呢?可以解答一下吗?

  • 你可以看下这个问题的回答https://ask.csdn.net/questions/7699081
  • 我还给你找了一篇非常好的博客,你可以看看是否有帮助,链接:随机森林回归和多输出元估计器回归对于多输出数据的回归及效果对比
  • 除此之外, 这篇博客: 特征多重共线对随机森林模型预测性能的影响研究中的 制造一些相关性很强的假数据 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  • 生成新的数据列’Grandmas Loan Agency’ ,它与列 'SONYMA DPAL/CCAL Amount’高度相关,数据展示如下:

    randoms = np.linspace(0.9, 1.1, len(dfmod))
    dfmod['Grandmas Loan Agency']=dfmod['SONYMA DPAL/CCAL Amount']*randoms
    corrDF=dfmod.corr()
    corrDF
    
    Original Loan AmountPurchase YearOriginal Loan To ValueSONYMA DPAL/CCAL AmountNumber of UnitsHousehold SizeProperty TypeCountyHousing TypeBond SeriesOriginal TermGrandmas Loan Agency
    Original Loan Amount1.0000000.337831-0.0569020.6620540.1127230.2383690.1010850.2328900.1241330.3259470.1844590.683658
    Purchase Year0.3378311.000000-0.152347-0.062682-0.0053650.0733430.1497630.1051000.1132730.9225740.0585120.030616
    Original Loan To Value-0.056902-0.1523471.000000-0.0917550.028671-0.033281-0.294189-0.189966-0.294904-0.167013-0.002098-0.105809
    SONYMA DPAL/CCAL Amount0.662054-0.062682-0.0917551.0000000.0502820.2021760.0259310.1600470.152185-0.0788720.1996890.993475
    Number of Units0.112723-0.0053650.0286710.0502821.000000-0.004223-0.003898-0.0274160.013539-0.006489-0.0029100.048050
    Household Size0.2383690.073343-0.0332810.202176-0.0042231.000000-0.0437920.1079120.0850880.0737330.0762690.207818
    Property Type0.1010850.149763-0.2941890.025931-0.003898-0.0437921.0000000.1976860.2241630.1584720.0304140.038477
    County0.2328900.105100-0.1899660.160047-0.0274160.1079120.1976861.0000000.1742620.1155250.0503210.167676
    Housing Type0.1241330.113273-0.2949040.1521850.0135390.0850880.2241630.1742621.0000000.1342840.0467030.167975
    Bond Series0.3259470.922574-0.167013-0.078872-0.0064890.0737330.1584720.1155250.1342841.0000000.0546990.009896
    Original Term0.1844590.058512-0.0020980.199689-0.0029100.0762690.0304140.0503210.0467030.0546991.0000000.207590
    Grandmas Loan Agency0.6836580.030616-0.1058090.9934750.0480500.2078180.0384770.1676760.1679750.0098960.2075901.000000
  • 您还可以看一下 Abel小智老师的嵌入式开发系统学习路线 从基础到项目 精品教程 工程师必备课程 物联网课程中的 如何使用回调函数?小节, 巩固相关知识点