Practical insights

Results

7 results (published only)

• Neural networks

  Start text classification with TF-IDF and a linear model

  You need to route airline customer complaints into 8 queues and the first idea is to fine-tune a Transformer.

  easyempirical finding

• Neural networks

  Fit the vectorizer vocabulary and IDF weights on training text only

  A notebook calls TfidfVectorizer().fittransform(alltexts) and then splits the matrix into train and test.

  easydocumented fact

• Neural networks

  Report per-class recall and macro averages for long-tail intent classifiers

  A banking chatbot intent model reports 94% accuracy over 60 intents, and most traffic falls into five intents.

  easypractitioner heuristic

• Neural networks

  Do not treat a t-SNE or UMAP picture of embeddings as evidence of separation

  A slide shows customer-message embeddings in 2D with neat colored clusters and claims the model separates segments.

  easydocumented fact

• Neural networks

  Quote an error bar that reflects the size of a small test set before comparing networks

  You compare two neural classifiers on a hold-out of 120 rows and one is ahead by four points.

  easyempirical finding

• Neural networks

  Print what a stock stop-word list removes before applying it to complaint or sentiment text

  A pipeline strips the packaged English stop-word list before TF-IDF on airline complaints because the tutorial did.

  easyempirical finding

• Neural networks

  Report metrics per detected language when messages mix Turkish and English

  Customer messages for an airline are Turkish, English and mixed, and one classifier reports a single accuracy.

  mediumpractitioner heuristic