`reduce_outliers`: add target-based outlier reduction ("reduce to X% outliers")

[pidefrem]

Feature request

reduce_outliers() requires manually specifying a threshold parameter. Users must run the method repeatedly, inspecting results each time, to find a threshold that achieves their desired outlier rate.

Add an outliers_percentage_target parameter that auto-searches for the optimal threshold:

# Current: manual trial-and-error
new_topics = topic_model.reduce_outliers(docs, topics, strategy="probabilities", threshold=0.3)
# Check outlier rate... too high. Try 0.2... too low. Try 0.25...

# Proposed: specify the target directly
new_topics = topic_model.reduce_outliers(docs, topics, strategy="probabilities",
                                          outliers_percentage_target=0.05)  # "at most 5% outliers"

Motivation

The trial-and-error workflow is evident in the 10+ open issues asking about reduce_outliers behavior:

• #1785 — reduce_outliers result not updated in model
• #1843 — persistent zero probability after reduce_outliers
• #1520 — clusters changed after reducing outliers
• #2114 — reduce_outliers removes stop_words/ngram_range effects

In production, users typically know their target — "I want at most 5% outliers" — not the internal threshold that achieves it.

Your contribution

I can submit a PR that adds an outliers_percentage_target parameter (float, 0–1): auto-search for the threshold that achieves the target outlier percentage using binary search. Works with all 4 existing strategies (probabilities, distributions, c-tf-idf, embeddings) and requires no changes to the underlying reduction logic.

Cannot set both threshold and outliers_percentage_target — raises ValueError. Backward compatible: defaults to None, existing behavior unchanged.

I've already been prototyping this in my fork, so I can open a PR quickly if the approach looks good to you.

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