Let's get some useful thresholds for models.  Generally, these thresholds are going to look a lot worse than they really are -- mostly because of labels we used to train are messy and incomplete.  We're targeting at least 70% precision, but we're likely to get that when we ask for 50% precision -- and in some cases, we'll still get it when we target even lower precision. 


So!  We're going to use ORES "threshold optimization" querying system.  We'll need to make a call for each topic in order to get an appropriate threshold:

* Culture.Biography.Biography* [[maximum recall @ precision >= 0.5](https://ores.wikimedia.org/v3/scores/enwiki/?models=articletopic&model_info=statistics.thresholds.%22Culture.Biography.Biography*%22.%22maximum%20recall%20@%20precision%20%3E=%200.5%22)]
```
              {
                "!f1": 0.925,
                "!precision": 0.996,
                "!recall": 0.863,
                "accuracy": 0.877,
                "f1": 0.662,
                "filter_rate": 0.759,
                "fpr": 0.137,
                "match_rate": 0.241,
                "precision": 0.5,
                "recall": 0.977,
                "threshold": 0.086
              }
```
* Culture.Biography.Women [[maximum recall @ precision >= 0.5](https://ores.wikimedia.org/v3/scores/enwiki/?models=articletopic&model_info=statistics.thresholds.%22Culture.Biography.Women%22.%22maximum%20recall%20@%20precision%20%3E=%200.5%22)]
```
              {
                "!f1": 0.993,
                "!precision": 0.995,
                "!recall": 0.99,
                "accuracy": 0.985,
                "f1": 0.572,
                "filter_rate": 0.981,
                "fpr": 0.01,
                "match_rate": 0.019,
                "precision": 0.501,
                "recall": 0.668,
                "threshold": 0.667
              }
```
* Culture.Media.Entertainment [[maximum recall @ precision >= 0.5](https://ores.wikimedia.org/v3/scores/enwiki/?models=articletopic&model_info=statistics.thresholds.%22Culture.Media.Entertainment%22.%22maximum%20recall%20@%20precision%20%3E=%200.5%22)]
```
              {
                "!f1": 0.998,
                "!precision": 0.998,
                "!recall": 0.998,
                "accuracy": 0.996,
                "f1": 0.47,
                "filter_rate": 0.997,
                "fpr": 0.002,
                "match_rate": 0.003,
                "precision": 0.503,
                "recall": 0.442,
                "threshold": 0.646
              }
```
* STEM.Mathematics [maximum recall @ precision >= 0.3](https://ores.wikimedia.org/v3/scores/enwiki/?models=articletopic&model_info=statistics.thresholds.%22STEM.Mathematics%22.%22maximum%20recall%20@%20precision%20%3E=%200.3%22)]
```
              {
                "!f1": 1.0,
                "!precision": 1.0,
                "!recall": 0.999,
                "accuracy": 0.999,
                "f1": 0.401,
                "filter_rate": 0.999,
                "fpr": 0.001,
                "match_rate": 0.001,
                "precision": 0.309,
                "recall": 0.571,
                "threshold": 0.903
              }
```

Here, we can see some diversity.  Culture.Biography.Biography* is easy to model and it's very common in the labeled data, so we can get very high precision and very high recall and a strict threshold.  STEM.Mathematics is on the other end of the spectrum.  There are very few math-related articles at all.  I've relaxed the minimum precision to 0.3 in order to get a threshold.