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name: ml-metrics-and-translation description: "Use NLTK classical ML, probability, language modeling, metrics, and translation/alignment APIs without reopening the source repo." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

NLTK ML, Metrics, and Translation

Use this sub-skill when a task needs NLTK's classical machine-learning, probability, n-gram language-modeling, evaluation metric, or machine-translation/alignment APIs.

Route here for

• Feature-dictionary classifier workflows: NaiveBayesClassifier, DecisionTreeClassifier, MaxentClassifier, SklearnClassifier, classifier accuracy, apply_features, and prob_classify interpretation.
• Vector-space clustering with KMeansClusterer, EMClusterer, GAAClusterer, and NLTK distance helpers.
• Count/probability utilities: FreqDist, ConditionalFreqDist, ProbDistI implementations, and conditional probability distributions.
• N-gram language models: padded_everygram_pipeline, Vocabulary, MLE, Lidstone, Laplace, WittenBellInterpolated, KneserNeyInterpolated, entropy, perplexity, and unseen-word handling.
• Evaluation metrics: precision, recall, f_measure, accuracy, edit/label distances, segmentation metrics, association measures, agreement, confusion matrices, and translation scores.
• Translation/alignment: Alignment, AlignedSent, IBM Models 1-5, PhraseTable, BLEU/GLEU/CHRF/NIST/METEOR/RIBES/LEPOR, and alignment error rate.

Route elsewhere first

• If the user is blocked by missing corpora or wants downloads/data paths, use the sibling data/downloader guidance first.
• If the user needs tokenization, POS tags, stemming, lemmatization, or sentiment feature extraction before modeling, use the preprocessing sibling first.
• If parse trees, grammars, chunking, dependency graphs, semantics, or inference are the main task, use the parsing/semantics sibling first.

Read the linked files

• Start with references/api-reference.md for public API contracts, required input shapes, optional dependencies, and score semantics.
• Use references/workflows.md for task recipes that combine classifiers, probability utilities, LMs, metrics, and alignments.
• Use references/troubleshooting.md when imports, feature dictionaries, smoothing, metric edge cases, optional binaries, or alignments fail.
• Run scripts/ml_translate_smoke.py for a tiny deterministic no-download smoke check in any environment where NLTK and its base dependencies are installed.

Operating rules

1. Keep examples tiny unless the user explicitly provides corpora or vectors. Many NLTK examples in the upstream tests use downloadable corpora; do not imply those are available.
2. Prefer pure-Python/base workflows first. Treat numpy, scipy, scikit-learn, python-crfsuite, MEGAM/TADM, Graphviz, Java wrappers, and corpora as optional unless the task requires them.
3. For classifiers, verify the user's data is [(featureset_dict, label), ...] and that prediction inputs are featureset_dict values, not raw strings or token lists.
4. For language models, make padding, vocabulary cutoff, smoothing, and <UNK> behavior explicit before interpreting entropy/perplexity.
5. For translation metrics and alignment models, confirm tokenized inputs and alignment index direction before scoring.