[scikit-learn] batch_size for small training sets

Greetings, I traing MLPRegressors using small datasets, usually with 10-50 observations. The default batch_size=min(200, n_samples) for the adam optimizer, and because my n_samples is always < 200, it is eventually batch_size=n_samples. According to the theory, stochastic gradient-based optimizers

Re: [scikit-learn] batch_size for small training sets

Small batch sizes are typically used to speed up the training (more iterations) and to avoid the issue that training sets usually don’t fit into memory. Okay, the additional noise from the stochastic approach may also be helpful to escape local minima and/or help with generalization performance