LDA Hyperparameters

In the CreateTrainingJob request, you specify the training algorithm. You can also specify algorithm-specific hyperparameters as string-to-string maps. The following table lists the hyperparameters for the LDA training algorithm provided by Amazon SageMaker. For more information, see How LDA Works.

Parameter Name	Description
num_topics	The number of topics for LDA to find within the data. Required Valid values: positive integer
feature_dim	The size of the vocabulary of the input document corpus. Required Valid values: positive integer
mini_batch_size	The total number of documents in the input document corpus. Required Valid values: positive integer
alpha0	Initial guess for the concentration parameter: the sum of the elements of the Dirichlet prior. Small values are more likely to generate sparse topic mixtures and large values (greater than 1.0) produce more uniform mixtures. Optional Valid values: Positive float Default value: 0.1
max_restarts	The number of restarts to perform during the Alternating Least Squares (ALS) spectral decomposition phase of the algorithm. Can be used to find better quality local minima at the expense of additional computation, but typically should not be adjusted. Optional Valid values: Positive integer Default value: 10
max_iterations	The maximum number of iterations to perform during the ALS phase of the algorithm. Can be used to find better quality minima at the expense of additional computation, but typically should not be adjusted. Optional Valid values: Positive integer Default value: 1000
tol	Target error tolerance for the ALS phase of the algorithm. Can be used to find better quality minima at the expense of additional computation, but typically should not be adjusted. Optional Valid values: Positive float Default value: 1e-8