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

新しい編集査読の絞り込みに増えた「貢献の品質」と「ユーザーの意図」の2種類のフィルターは、既存のものと機能が異なります. これらは推測により、編集に問題があるかないか、また変更を行った意図は善意か悪意か予測するものです. このツール独自の特徴を覚えると活用の近道になります.

これらのフィルターを使えるウィキは限定されます.

基本は機械学習
品質と意図予測フィルターを演算するORESは機械学習プログラムで、人間がこれまでに判定した編集の膨大な蓄積に基づいて訓練されました. 機械学習という処理能力の高い技術は、人間の代理で機械が特定の判断を再現します.

品質と意図フィルターは「破壊的」と「善意」を判定する ORES モデルをサポートするウィキ限定で利用できます. ORES 「破壊」モデルは編集の品質、「善意」モデルは同じく意図が対象です.

ORES を有効にするには、該当するウィキにおける編集をボランティアが判定しなければなりません. そのプロセスとユーザーのウィキで取り組む方法を別のページで説明しています.

ツールの選び方
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.

Why would anyone choose to use a tool that's less accurate? Because such accuracy can come at a cost.

予測の正解率を上げるには (〈精度〉優先)
[[File:RC-quality-filters-diagram.png|alt=This conceptual diagram illustrates how the Quality filters relate to one another.|thumb|400x400px|この概念図を使って、異なるウィキごとに連動する品質フィルターの働きを説明します.

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).

For space reasons, the diagram doesn't accurately reflect scale. ]] 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.

ターゲットの数を増やすには (〈ヒット率〉優先)
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.)

精度もヒット率も求めるには (強調表示)


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.)


 * 例: 悪意の拾い出しを最大にして、最悪/深刻度大を強調表示:
 * フィルターを起動して初期設定から、
 *  をチェックして品質の幅を最大に.
 * At the same time, highlight —without checking the filter boxes— ', in yellow, and ', in red.


 * 品質の幅を最大に設定したため、拾い出す悪意の編集は多くなります (「ヒット率」優先). しかし、視覚的に黄色、赤、オレンジ (= 赤と黄色を混ぜた色) を視覚的にスキャンすることで、いちばん深刻な問題が見つかりやすく、先に取りかかれるはずです. (ヘルプは「フィルターを使わず強調表示する」を参照. )

良い編集の探し方 (編集者を褒めよう)


善意の行いは自然と目に入ってきますよね! 良い編集を探すのも簡単なのです.

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.


 * サンプル: 初学者の善意に感謝を伝える
 * ゴミ箱アイコンを押して検索条件をクリア. 'と'にチェックを入れる.
 * にチェックを入れ、品質を優先.
 * 'と'にチェック、ユーザーの登録と経験を絞り込み (これで登録利用者の編集に限定).
 * の横のマーカーアイコンを押してグリーンを選択.
 * 結果は初心者 (登録後4日未満で編集実績10件未満) と初学者 (活動歴30日未満、編集実績500件未満) に絞り込まれました. 後者のみグリーンで示されて見分けがつきます.

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.

Filters list
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.