<p>Hyperparameter tuning is typically done on the validation set of a train-val-test split, where each split will have something along the lines of 70%, 10%, and 20% of the entire dataset respectively. As a baseline, random search can be used while <a href="https://arxiv.org/abs/1206.2944" rel="nofollow noreferrer">Bayesian optimization with Gaussian processes</a> has been shown to be more compute efficient. <a href="https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html" rel="nofollow noreferrer">scikit-optimize</a> is a good package for this.</p>
how to do hyperparameter optimization in large data?
<p>I almost finished my time series model, collected enough data and now I am stuck at hyperparameter optimization.</p>
<p>And after lots of googling I found new &amp; good library called ultraopt, but problem is that how much amount of fragment of data should I use from my total data (~150 GB) for hyperparameter tuning. And I want to try lots of algorithm and combinations, is there any faster and easy way?</p>
<p>Or</p>
<p>Is there any math involved, something like,
mydata = 100%size</p>
<p>hyperparameter optimization with 5% of mydatasize,</p>
<p>optimized hyperparameter *or+ or something with left 95% of datasize   #something like this</p>
<p>To get a similar result as full data used for optimization at a time. Is there any shortcut for these?</p>
<p>I am using Python 3.7,
CPU: AMD ryzen5 3400g,
GPU: AMD Vega 11,
RAM: 16 GB</p>