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TF-IDF Scoring is a medium coding interview question covering nlp, machine learning, strings — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement TF-IDF (Term Frequency - Inverse Document Frequency) scoring for a small corpus.
TF(t, d) = count(t in d) / total words in d IDF(t) = log(N / (1 + df(t))) -- N=total docs, df=docs containing term TF-IDF(t, d) = TF(t, d) * IDF(t)
Used in search engines and as a text feature extractor before neural embeddings existed.
Return a dict of {term: tfidf_score} for a given document and corpus.
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