Question

A data science team at a media company wants to build a BigQuery ML logistic regression model to classify news articles by topic. The raw training table contains a single free-text column called 'body'. They want the model to learn from word-frequency signals in the text, and they need the same text preprocessing to be applied automatically at prediction time without re-writing transformation SQL. Which approach best meets these requirements?

Accepted Answer

Use the ML.TF_IDF or ML.NGRAMS-based feature engineering functions inside a TRANSFORM clause when creating the model so the text preprocessing is stored with the model and reapplied automatically during prediction.

Answer

Precompute a TF-IDF matrix in a separate SQL job, store it in a staging table, and train the model on that table; re-run the same SQL manually before each prediction batch.

Answer

Export the text to Vertex AI, train a custom AutoML text model, and import predictions back into BigQuery via scheduled queries.

Answer

Pass the raw 'body' column directly as a feature to CREATE MODEL; BigQuery ML automatically tokenizes and vectorizes any STRING column into TF-IDF features.