How deeply does Machine Learning System Design cover bag-of-words model?

Defines the representation and its word-order limitation, then points to longer N-grams and alternatives rather than implementation details.

Bag-of-words model

As explained in Machine Learning System Design

A bag-of-words representation describes text using counts or indicators of its words while disregarding word order.

A bag-of-words model turns text into machine-learning features by recording which words appear or how often they appear, while ignoring their order. It offers a straightforward starting point for sequential data, but its simplicity removes information that may matter to the prediction task. section 11.1.2

Use the representation with these tradeoffs in mind:

Choose it when a simple text representation is sufficient for the downstream machine-learning algorithm. section 11.1.2
Expect word-order information to disappear; longer N-grams can partially recover local sequence information compared with single-word unigrams. section 11.1.2
Compare it with TF-IDF, BM25, or pretrained language-model representations such as BERT when the task needs a different text encoding. section 11.1.2

How Machine Learning System Design covers it

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Also explained in

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Natural Language Processing in Action, Second EditionBag-of-words is a text representation that records word occurrences while disregarding the order and syntactic relationships of the words.
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Deep Learning with Python, Third EditionA text model that ignores token order and represents a document by which words occur in it, often with per-word weights.
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Data Without LabelsBag-of-words is a text representation that uses word occurrences or frequencies as features while disregarding word order and context.
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Deep Learning with R, Third EditionA bag-of-words model represents text as an unordered collection of tokens and assigns learned weights to token presence indicators.

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Related concepts

sequence modelcontrasts with · A sequence model processes ordered data while accounting for dependencies among its positions to produce predictions or representations.
n-gram modeladdresses · An n-gram model is a probabilistic language model that estimates sequence probabilities using contiguous sequences of a fixed number of tokens, typically to predict a token from preceding context.
latent Dirichlet allocationused by · Latent Dirichlet allocation is a generative probabilistic topic model representing documents as mixtures of latent topics and topics as distributions over words.
parameteruses · A model parameter is a numerical value, such as a weight or bias, that is learned or set to determine how a model computes outputs.
term frequency-inverse document frequencycontrasts with · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.