Help:New filters for edit review/Quality and Intent Filters/zh

新的编辑审阅过滤器引入了两个过滤器组——贡献质量、用户意图，两者的工作方式与其他审阅过滤器不同. 这两个组中的过滤器分别提供：编辑是否可能有问题以及操作人是否善意的概率预测. 了解这些特有工具的运作方式有助您更有效地使用它们.

这些过滤器目前只在部分wiki上可用.

基于机器学习
质量和意图过滤器的预测由ORES计算，它是一个机器学习程序，基于之前的大量人类编辑训练而成. 机器学习是一种强大的计数，可使机器学会有限的人类判断力.

质量和意图过滤器只在支持“破坏”和“诚信”ORES“模型”的wiki上可用. ORES“破坏”模型“damaging”提供质量预测，“诚信”模型提供意图预测.

启用ORES需要志愿者对相关wiki上的修订内容评分. 这里介绍了其过程，以及如何在你的wiki上开始.

选择正确的工具
Looking at the Quality and Intent filters, you may notice something different about them. Unlike filters in other groups, the various options don’t target different edit properties. Instead, many of them target the same property, but offer different levels of accuracy.

为什么有人要使用不太精确的工具？因为精确有代价.

增加预测概率（较“精确”）
[[File:RC-quality-filters-diagram.png|alt=This conceptual diagram illustrates how the Quality filters relate to one another.|thumb|400x400px| This conceptual diagram illustrates how the Quality filters relate to one another on many wikis (performance varies).

As you can see, the  filter captures results composed almost entirely of problem edits (high precision). But it captures only a small portion of all problem edits (low recall). Notice how everything in ' (and ') is also included in the broader , which provides high recall but low precision (because it returns a high percentage of problem-free edits).

You may be surprised to see that ' overlaps with '. Both filters cover the indeterminate zone between problem and problem-free edits in order to catch more of their targets (broader recall).

因空间原因，例图不能反映其规模. ]] The more “accurate” filters on the menu return a higher percentage of correct versus incorrect predictions and, consequently, fewer false positives. (In the lingo of pattern recognition, these filters have a higher “precision”.) They achieve this accuracy by being narrower, stricter. When searching, they set a higher bar for probability. The downside of this is that they return a smaller percentage of their target.


 * Example: The  filter is the most accurate of the Quality filters. Performance varies from wiki to wiki, but on English Wikipedia its predictions are right more than 90% of the time. The tradeoff is that this filter finds only about 10% of all the problem edits in a given set —because it passes over problems that are harder to detect. The problems this filter finds will often include obvious vandalism.

找到更多目标（更模糊）
If your priority is finding all or most of your target, then you’ll want a broader, less accurate filter. These find more of what they’re looking for by setting the bar for probability lower. The tradeoff here is that they return more false positives. (In technical parlance, these filters have higher “recall”, defined as the percentage of the stuff you’re looking for that your query actually finds.)


 * Example: The  filter is the broadest Quality filter. Performance varies on different wikis, but on English Wikipedia it catches about 82% of problem edits. On the downside, this filter is right only about 15% of the time.


 * If 15% doesn’t sound very helpful, consider that problem edits actually occur at a rate of fewer than 5 in 100—or 5%. So 15% is a 3x boost over random. And of course, patrollers don’t sample randomly; they’re skilled at using various tools and clues to increase their hit rates. Combined with those techniques,  provides a significant edge.

(As noted above, ORES performs differently on different wikis, which means that some are less subject to the tradeoffs just discussed than others. On Polish Wikipedia, for example, the ' filter captures 91% of problem edits, compared to 34% with the corresponding filter on English Wikipedia. Because of this, Polish Wikipedia does not need—or have—a broader ' filter.)

两全其美（高亮功能）


The filtering system is designed to let users get around the tradeoffs described above. You can do this by filtering broadly while Highlighting the information that matters most.

To use this strategy, it’s helpful to understand that the more accurate filters, like ', return results that are a subset of the less accurate filters, such as '. In other words, all “Very likely” results are also included in the broader . (The diagram above illustrates this concept.)


 * Example: Find almost all damage while emphasizing the worst/most likely:


 * With the default settings loaded,


 * Check the broadest Quality filter, .


 * At the same time, highlight —without checking the filter boxes— ', in yellow, and ', in red.


 * Because you are using the broadest Quality filter, your results will include most problem edits (high “recall”). But by visually scanning for the yellow, red and orange (i.e., blended red + yellow) bands, you will easily be able to pick out the most likely problem edits and address them first. (Find help on using highlights without filtering.)

找到好编辑（并鼓励）


Good faith is easy to find, literally! So are good edits.

The ' filter and the ' (Quality) filter give you new ways to find and encourage users who are working to improve the wikis. For example, you might use the ' filter in combination with the ' filter to thank new users for their good work.


 * Example: Thank good-faith new users
 * Clear the filters by clicking the Trashcan. Then select the ' and ' filters.
 * Check the Quality filter .
 * Check the User Registration and Experience filters ' and ' (this has the hidden effect of limiting your results to registered users).
 * Highlight the  filter, in green.


 * All edits in your results will be good edits by Newcomers (users with fewer than 10 edits and 4 days of activity) and Learners (users with fewer than 500 edits and 30 days of activity). The green highlight lets you easily distinguish between the two.

Good is everywhere!
The “good” filters mentioned above are both accurate and broad, meaning they aren’t subject to the tradeoffs described in the previous section (they combine high “precision” with high “recall”). These filters are correct about 99% of the time and find well over 90% of their targets. How can they do that?

The happy answer is that the “good” filters perform so well because good is more common than bad. That is, good edits and good faith are much, much more plentiful than their opposites—and therefore easier to find. It may surprise some patrollers to hear this, but on English Wikipedia, for example, one out of every 20 edits has problems, and only about half those problematic edits are intentional vandalism.

过滤器列表
On wikis where Quality and Intent Filters are deployed, some filters may be missing due to a better quality of predictions. The better ORES performs on a wiki, the fewer filter levels are needed.