# Randomized search on big dataset

I have a dataset of 700,000 rows that Im applying random search on. My parameter grid looks like this:

parameters_gbc = {
"loss":["deviance"],
"learning_rate": [0.01, 0.025, 0.075, 0.1, 0.2],
"max_depth":[3,5,8,10],
"max_features":["log2","sqrt"],
"criterion": ["friedman_mse",  "mae"],
"subsample":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],
"n_estimators":[10, 100, 200, 350]
}


I am using 20 iterations for the search:

n_iter_search = 20
random_search_gbc = RandomizedSearchCV(gbc, param_distributions=parameters_gbc,
n_iter=n_iter_search)


I'm not very experienced with this, so how long should I expect the search to be over? How can I make my search more time effective since it has been running for the past 30 hours?

With $$20$$ iterations you are exploring $$\tfrac{20}{1 \times 4 \times 4 \times 2 \times 2 \times 7 \times 4} \approx 1\%$$ of the parameter grid, so you would be exploring only a small fraction of the search space. You would need many more iterations then this.
Randomized search can take long for a big dataset, this is quite normal. You should check the parameter n_jobs which helps to parallelize the search and make it faster (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html).