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Naive Bayes and TensorFlow

TensorFlow/Keras has no built-in classical Naive Bayes estimator. The algorithm estimates priors and class-conditional distribution parameters, then compares log-posterior scores. It does not require a neural-network training loop.

Why the previous TensorFlow example was removed​

The removed page combined TensorFlow Probability objects, a custom dense count implementation, an external CSV file, and a scikit-learn vectorizer fitted before the split. Besides being non-reproducible, that vocabulary step leaked test-document information and the custom classifier did not represent a complete maintained estimator API.

Use a pipeline such as CountVectorizer followed by MultinomialNB for count-based text. Splitting raw documents before fitting the pipeline ensures that vocabulary, document frequencies, smoothing parameters, and class priors come from training data only.

Framework boundaries​

TensorFlow is appropriate when the intended model is a trainable neural classifier or when Naive Bayes-like factors are part of a deliberately specified probabilistic program. In that case, document the distributions, dependencies, priors, optimization or inference method, and numerical checks. Do not present an ad hoc tensor calculation as the standard Naive Bayes API.

If TensorFlow creates embeddings upstream, remember that dense signed embeddings do not satisfy the non-negative count assumptions of MultinomialNB, and correlated embedding dimensions undermine the conditional-independence approximation. Compare alternatives empirically with leakage-safe validation.

Use the scikit-learn example in this section for classical Naive Bayes. Preserve sparse matrices, tune smoothing within training folds, and verify probability calibration separately from classification accuracy.