正确的回归模型

Question

我正在尝试从功能很少的数据集中预测预留次数。功能既是分类的又是连续的。

因变量保留如下所示：我的数据集大小约为917 obs。

const injectedJavaScript: = `
    let currentStep = "login";
    if(currentStep === 'login') {
      document.getElementById('userID').value =  '${
        this.props.username
      }'; document.getElementById('userPassword').value = '${
        this.props.password
      }'; document.getElementById('login').click();
      currentStep = 'agreeToTerms';
    } else if(currentStep === 'agreeToTerms' && window.location.href === "https://www.example.com") {
      currentStep = 'download';
      document.getElementsByClassName('button')[0].click();
    }
    `;


  <WebView
        ignoreSslError={true}
        source={{ uri: "https://www.example.com" }}
        style={{ marginTop: 20, height: 400, width: 400 }}
        injectedJavaScript={injectedJavaScript}
        javaScriptEnabledAndroid={true}
        onError={() => this.webviewError(e)}
        startInLoadingState={true}
        scalesPageToFit={true}
      />

当我绘制因变量的直方图时，我得到了

因此，我使用了对数变换来消除某些偏斜。

为array([ 1, 7, 17, 2, 2, 13, 8, 11, 9, 4, 4, 3, 5, 2, 5, 7, 3, 12, 9, 13, 5, 2, 11, 13, 14, 19, 9, 11, 3, 6, 7, 10, 1, 6, 5, 10, 8, 5, 4, 3, 2, 10, 10, 10, 8, 13, 16, 6, 4, 6, 3, 11, 10, 1, 18, 7, 2, 12, 17, 4, 2, 19, 3, 4, 17, 13, 10, 2, 10, 1, 3, 4, 20, 3, 2, 1, 3, 5, 8, 8, 4, 3, 13, 3, 3, 5, 4, 17, 7, 6, 10, 5, 3, 9, 9, 8, 1, 5, 17, 5, 10, 9, 2, 7, 13, 2, 9, 1, 15, 13, 10, 4, 2, 4, 5, 4, 3, 3, 10, 4, 7, 5, 13, 12, 7, 5, 6, 9, 5, 11, 7, 1, 4, 12, 4, 3, 11, 1, 4, 4, 3, 7, 4, 11, 4, 1, 9, 2, 10, 10, 3, 4, 4, 3, 2, 7, 10, 7, 6, 1, 3, 19, 9, 3, 8, 20, 1, 12, 9, 13, 13, 2, 9, 4, 9, 2, 5, 6, 18, 3, 6, 8, 6, 4, 5, 13, 4, 8, 9, 5, 4, 8, 5, 2, 1, 6, 8, 3, 6, 4, 2, 6, 11, 5, 1, 5, 1, 5, 11, 11, 9, 3, 12, 2, 2, 9, 19, 7, 13, 13, 9, 2, 1, 1, 4, 3, 4, 9, 1, 25, 12, 8, 5, 18, 3, 1, 6, 17, 7, 4, 6, 9, 8, 10, 3, 8, 12, 5, 4, 4, 1, 9, 21, 4, 3, 3, 7, 13, 5, 12, 8, 8, 6, 3, 6, 7, 5, 3, 7, 3, 14, 3, 5, 2, 14, 16, 3, 8, 6, 13, 9, 3, 5, 4, 9, 4, 12, 12, 4, 9, 8, 11, 5, 13, 3, 2, 5, 4, 2, 1, 8, 8, 18, 11, 2, 5, 13, 4, 1, 2, 4, 1, 2, 2, 12, 2, 6, 19, 7, 20, 2, 10, 2, 9, 12, 9, 8, 1, 4, 8, 8, 12, 4, 8, 1, 3, 6, 9, 4, 3, 8, 2, 7, 15, 6, 5, 10, 6, 4, 3, 12, 5, 4, 13, 7, 2, 8, 5, 2, 4, 3, 14, 12, 3, 4, 3, 2, 15, 6, 14, 12, 11, 9, 5, 5, 7, 11, 10, 7, 9, 9, 7, 11, 5, 11, 3, 2, 5, 17, 5, 2, 6, 1, 10, 3, 13, 19, 5, 1, 3, 5, 3, 5, 6, 3, 9, 8, 2, 3, 2, 3, 7, 4, 9, 5, 1, 6, 14, 4, 8, 17, 13, 7, 1, 4, 5, 10, 5, 6, 2, 12, 5, 9, 3, 9, 9, 1, 5, 1, 2, 2, 5, 1, 4, 4, 13, 4, 25, 9, 10, 4, 3, 9, 13, 13, 2, 9, 2, 12, 4, 1, 20, 9, 10, 2, 5, 4, 10, 2, 6, 1, 7, 7, 7, 4, 8, 4, 3, 4, 13, 8, 3, 13, 12, 19, 9, 3, 2, 6, 7, 13, 8, 16, 7, 3, 11, 4, 10, 9, 12, 2, 8, 5, 2, 3, 4, 2, 1, 11, 5, 4, 2, 8, 12, 7, 5, 7, 7, 4, 6, 18, 2, 1, 6, 15, 11, 2, 5, 8, 3, 5, 9, 11, 5, 8, 6, 20, 1, 10, 3, 7, 1, 3, 5, 4, 4, 10, 11, 6, 1, 5, 4, 1, 2, 10, 4, 4, 11, 20, 5, 3, 2, 7, 8, 2, 10, 5, 1, 18, 5, 10, 5, 3, 8, 15, 2, 1, 14, 10, 7, 3, 5, 9, 3, 4, 21, 14, 1, 2, 1, 2, 4, 11, 9, 7, 6, 9, 18, 4, 6, 18, 12, 12, 4, 6, 3, 3, 9, 5, 12, 15, 3, 7, 3, 7, 4, 2, 15, 14, 7, 10, 5, 5, 5, 9, 3, 6, 3, 1, 11, 1, 5, 25, 8, 2, 24, 1, 12, 1, 6, 8, 5, 13, 4, 3, 3, 13, 4, 4, 18, 7, 13, 2, 8, 3, 4, 9, 2, 13, 12, 4, 5, 10, 9, 15, 1, 8, 8, 15, 10, 1, 9, 2, 2, 2, 2, 3, 6, 17, 7, 5, 5, 6, 12, 1, 8, 3, 1, 11, 4, 7, 8, 15, 6, 11, 9, 9, 13, 2, 3, 5, 3, 5, 12, 4, 4, 8, 7, 12, 2, 2, 4, 4, 12, 8, 11, 10, 6, 5, 1, 4, 2, 7, 3, 5, 15, 12, 12, 2, 9, 7, 4, 4, 5, 15, 5, 8, 13, 7, 2, 8, 12, 2, 13, 6, 24, 14, 3, 4, 1, 2, 8, 7, 5, 12, 8, 2, 6, 3, 7, 5, 2, 7, 3, 3, 1, 9, 9, 3, 12, 3, 2, 11, 11, 6, 3, 9, 12, 4, 8, 7, 5, 2, 10, 19, 1, 1, 10, 6, 2, 4, 2, 4, 4, 3, 7, 13, 9, 6, 2, 2, 2, 5, 13, 12, 2, 13, 12, 11, 10, 5, 8, 8, 15, 12, 3, 3, 9, 4, 6, 13, 15, 4, 7, 1, 12, 10, 9, 7, 3, 7, 4, 9, 2, 10, 2, 11, 10, 14, 3, 13, 8, 3, 12, 11, 10, 7, 5, 3, 3, 11, 3, 13, 9, 10, 20, 7, 12, 3, 6, 6, 18, 3, 10, 11, 10, 5, 6, 11, 4, 6, 7, 9, 13, 1, 14, 14, 13, 4, 3, 8, 5, 7, 14, 13, 13, 12, 8, 11, 12, 9, 8, 9, 4, 5, 4, 7, 5, 2, 3, 1, 7, 2, 1, 13, 5, 19, 9, 6, 9, 7])

现在分布图如下：

某些功能。

y=np.log(df["reservartions"].values)

由于actual_price和Recommended_price具有巨大的相关性，因此我创建了两者的差价，并删除了actual_price和建议价格。

但是运行线性回归或随机森林回归后，我得到的结果都很差，R2均为0.12。

这表明模型显然不能很好地预测和拟合。

我的因变量显然是一个正变量。线性回归还正确吗？我应该使用泊松回归吗？日志转换有意义吗？