Monument (www.monument.ai) enables you to quickly apply algorithms to data in a no-code interface. But, after you drag the algorithms onto data to generate predictions, you need to decide which algorithm or combination of algorithms is most reliable for your task.

使用Monument( www.monument.ai )，您可以在无代码界面中快速将算法应用于数据。 但是，将算法拖到数据上以生成预测后，需要确定哪种算法或算法组合最适合您的任务。

In the ocean temperature tutorial, we cleaned open remote sensing data and fed the data into Monument in order to forecast future ocean temperatures. In that case, we used visual inspection to evaluate the accuracy of different algorithms, which was possible because the historical data roughly formed a sine curve. Visual inspection is one tool in the data science toolbox, but there are other tools as well.

在海洋温度教程中，我们清理了开放的遥感数据并将其输入到Monument中，以预测未来的海洋温度。 在那种情况下，我们使用视觉检查来评估不同算法的准确性，这是有可能的，因为历史数据大致形成了正弦曲线。 视觉检查是数据科学工具箱中的一种工具，但是还有其他工具。

The Validation Error Rate is another useful tool in cases where you want to get more fine-grained or where visual inspection does not yield obvious insights. There are other error functions that can be used, but Validation Error Rate is the default error function in Monument.

在您希望获得更细粒度或视觉检查无法产生明显见解的情况下，“验证错误率”是另一个有用的工具。 可以使用其他错误函数，但“验证错误率”是Monument中的默认错误函数。

验证错误率是多少？为什么重要？ (What Is The Validation Error Rate And Why Is It Important?)

The Validation Error Rate measures the distance between “out of sample” values and estimates produced by the algorithm. You can find this metric in the INFO box in the lower-left corner of the MODEL workspace.

验证错误率可衡量“样本外”值与算法产生的估计值之间的距离。 您可以在MODEL工作区左下角的INFO框中找到该指标。

As a general rule of thumb, the “more negative” your Validation Error Rate is, the more accurate the model is. Negative infinity would be a perfect model. In the real world, as we will see with our ocean temperature data, sometimes the best you can do is a small, but nevertheless positive number.

通常，验证错误率越“负”，则模型越准确。 负无穷大将是一个完美的模型。 在现实世界中，正如我们将通过海洋温度数据所看到的那样，有时您能做的最好的事情虽然很小，但仍然是正数。

Currently, Monument only displays one Validation Error Rate at a time. To view the Validation Error Rate for other algorithms that you have trained, click the drop-down arrow on the right side of the algorithm pill and select SHOW ERROR RATE.

当前，纪念碑仅一次显示一个验证错误率。 要查看您已训练的其他算法的验证错误率，请单击算法丸右侧的下拉箭头，然后选择显示错误率。

To compare the performance of the models, I have pasted below a table of all the Validation Error Rates applied to the ocean temperatures data, sorted from lowest to highest.

为了比较模型的性能，我在下表中粘贴了应用于海洋温度数据的所有“验证错误率”，从最低到最高排序。

As we discovered in the tutorial, with default parameters, AR and G-DyBM perform the best on the cleaned and transformed data.

正如我们在本教程中发现的那样，使用默认参数，AR和G-DyBM在清理和转换后的数据上表现最佳。

如何提高算法性能 (How To Improve Algorithm Performance)

Typically, we can improve the Validation Error Rate — i.e. make it “more negative” — by adjusting the algorithms’ parameters. You can access an algorithm’s parameters by selecting PARAMETERS in the algorithm pill drop-down.

通常，我们可以通过调整算法参数来提高“验证错误率”，即使其“更负”。 您可以通过在算法药丸下拉列表中选择“参数”来访问算法的参数。

Choosing which parameters to edit to improve performance depends heavily on your business objectives and the nature of the data you’re looking at. We will cover common cases in future tutorials, but the best approach is to experiment yourself to develop an intuition around which parameters most improve results for different kinds of data.

选择要编辑哪些参数以提高性能的方法很大程度上取决于您的业务目标和所查看数据的性质。 我们将在以后的教程中介绍一些常见的案例，但是最好的方法是尝试自己，以开发出一种直觉，即参数可以最有效地改善不同类型数据的结果。

Certain algorithms allow for automated parameter adjustment. In Monument, the LSTM and LightGBM algorithms also have “AutoML,” which is short for Automated Machine Learning. AutoML automatically adjusts an algorithm’s parameters to optimize performance. You can select AUTOML from the algorithm drop-down to access these capabilities.

某些算法允许自动调整参数。 在Monument中，LSTM和LightGBM算法还具有“ AutoML”，这是自动机器学习的缩写。 AutoML自动调整算法参数以优化性能。 您可以从算法下拉列表中选择AUTOML以访问这些功能。

For example, when we run AutoML on the HABSOS data, we can lower the Validation Error Rate by 0.04 from 3.273 to 3.233. Not a huge improvement on this particular data, but an improvement nonetheless. Often, the gains are much greater.

例如，当我们在HABSOS数据上运行AutoML时，我们可以将验证错误率从3.273降低0.04到3.233。 在此特定数据上不是很大的改进，但是还是有改进。 通常，收益会更大。

There are other reports within Monument that we can use to improve algorithm performance, including, dependent variables, forecast training convergence, and feature importance. We’ll explore these topics in future tutorials.

Monument内还有其他报告可用于改善算法性能，包括因变量，预测训练收敛性和功能重要性。 我们将在以后的教程中探讨这些主题。