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Stochastic models in sklearn
In scikit-learn, stochastic models refer to a class of algorithms that involve randomness in their training process. These models are typically used for large-scale datasets when it is computationally expensive or infeasible to process the entire dataset at once. Instead, they perform updates on a subset of the data or use random sampling techniques.
Machine Learning – Part 4 – Gradient Based Models (Pre-Cursor To Deep Learning)
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This is a variant of the K-means clustering algorithm that performs updates on randomly selected subsets (mini-batches) of the data instead of the entire dataset. It is particularly useful when dealing with large datasets where the entire dataset cannot fit into memory. Scikit-learn provides the MiniBatchKMeans class for mini-batch K-means clustering.

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