解释统计模型指标
interpret statistical model metrics
你知道如何解读RAE和RAE值吗？我知道 COD 接近 1 是一个好兆头。这是否表明提升决策树回归是最好的？
RAE 和 RSE 接近 0 是一个好兆头……您希望误差尽可能低。有关评估模型的更多信息，请参阅 this article。来自该页面：
The term "error" here represents the difference between the predicted value and the true value. The absolute value or the square of this difference are usually computed to capture the total magnitude of error across all instances, as the difference between the predicted and true value could be negative in some cases. The error metrics measure the predictive performance of a regression model in terms of the mean deviation of its predictions from the true values. Lower error values mean the model is more accurate in making predictions. An overall error metric of 0 means that the model fits the data perfectly.
是的，根据您当前的结果，提升决策树的效果最好。我不太了解您的工作细节，无法确定这是否足够好。老实说可能是。但如果您确定不是，您也可以调整 "Boosted Decision Tree Regression" 模块中的输入参数，以尝试获得更好的结果。 "ParameterSweep" module can help with that by trying many different input parameters for you and you specify the parameter that you want to optimize for (such as your RAE, RSE, or COD referenced in your question). See this article 进行简要说明。希望这对您有所帮助。
P.S。我很高兴你正在调查维斯特洛的黑碳含量……我敢肯定瑟曦根本不在乎。
你知道如何解读RAE和RAE值吗？我知道 COD 接近 1 是一个好兆头。这是否表明提升决策树回归是最好的？
RAE 和 RSE 接近 0 是一个好兆头……您希望误差尽可能低。有关评估模型的更多信息，请参阅 this article。来自该页面：
The term "error" here represents the difference between the predicted value and the true value. The absolute value or the square of this difference are usually computed to capture the total magnitude of error across all instances, as the difference between the predicted and true value could be negative in some cases. The error metrics measure the predictive performance of a regression model in terms of the mean deviation of its predictions from the true values. Lower error values mean the model is more accurate in making predictions. An overall error metric of 0 means that the model fits the data perfectly.
是的，根据您当前的结果，提升决策树的效果最好。我不太了解您的工作细节，无法确定这是否足够好。老实说可能是。但如果您确定不是，您也可以调整 "Boosted Decision Tree Regression" 模块中的输入参数，以尝试获得更好的结果。 "ParameterSweep" module can help with that by trying many different input parameters for you and you specify the parameter that you want to optimize for (such as your RAE, RSE, or COD referenced in your question). See this article 进行简要说明。希望这对您有所帮助。
P.S。我很高兴你正在调查维斯特洛的黑碳含量……我敢肯定瑟曦根本不在乎。