Hyperparameter tuning is the process of adjusting the configuration settings of an ML algorithm to optimize performance and improve the quality of the model’s output. These parameters are set prior to or during the training process, and are distinct from the parameters learned during training.

In LDA topic modeling, hyperparameters that can be tuned include the number of topics (K), the number of passes or iterations, and the alpha and beta values, which influence the resulting coherence score.