Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

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BlackTom AI · Accepted Answer

The question asks: Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

Option 1: 'A human' — This is the correct line of thinking in practice. The number of topics K in LDA is a hyperparameter that researchers or practitioners choose, often guided by domain knowledge, prior experiments, interpretability considerations, or evaluation metrics. Humans typically decide or set this value before running the model, and then the model infers topic distributions given that choice.

Option 2: 'A machine' — While automated methods can help search over possible K values (e.g., via cross-validation, perplexity, or coherence scores), the ultimate decision about which K to use is not inherently decided by the model itself. The machine can assist in selecting candidates or assessing metrics, but the selection process relies on an interpretation or preference set by a human or an automated tuning system designed by humans. The phrasing implies the machine alone makes the choice without human guidance, which oversimplifies how hyperparameter selection is typically conducted.

Additional nuance: In some workflows, you might see automated hyperparameter tuning procedures (grid search, Bayesian optimization) that propose several K values and evaluate them, effectively transferring decision power from a single human to an optimization algorithm. However, even in those cases, there is still an objective to optimize and a stopping rule defined by humans, meaning the ultimate governance over the process is not purely 'a machine' autonomously deciding in isolation.

In summary, the conventional understanding is that humans set or select the number of topics K before fitting LDA, even though automated tools can assist in evaluating candidate values. The other option misrepresents the role of machines in this hyperparameter decision.

SP2025.B69.DAT.562.24 COMPLETE Quiz #4 AFTER VIDEO 3: Latent Dirichlet Allocation (LDA)

Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

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Similar Questions

The output of the Latent Dirichlet Allocation (LDA) topic model is:

The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

The Latent Dirichlet Allocation (LDA) estimates the distribution of topics in documents and the distribution of words in topics

What is topic modeling? Choose one best answer:

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