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While using a grid of parameter settings is currently the most widely used method for parameter optimization, other search methods have more favorable properties. The param_distribs will contain the parameters with arbitrary choice of the values I have a few questions concerning randomized grid search in a random forest regression model

My parameter grid looks like this The ```rf_clf`` is the random forest model object I''m trying to use xgboost for a particular dataset that contains around 500,000 observations and 10 features

I'm trying to do some hyperparameter tuning with randomizedseachcv, and the performanc.

I have removed sp_uniform and sp_ randint from your code and it is working well from sklearn.model_selection import randomizedsearchcv import lightgbm as lgb np. I am attempting to get best hyperparameters for xgbclassifier that would lead to getting most predictive attributes I am attempting to use randomizedsearchcv to iterate and validate through kfold. Your train/cv set accuracy in gridsearch is higher than train/cv set accuracy in randomized search

The hyper parameters should not be tuned using the test set, so assuming you're doing that properly it might just be a coincidence that the hyper parameters that were chosen from randomized search performed better on the test set. Pipeline = pipeline(steps) # do search search = randomizedsearchcv(pipeline, param_distributions=param_dist, n_iter=50) search.fit(x, y) print search.grid_scores_ if you just run like this, you'll get the following error Invalid parameter kernel for estimator pipeline is there a good way to do this in sklearn? Have tried with iris data and with dummy data from several configurations of make_classification

Every single time the result of your posted code is identical with cv_best_score_

Please provide a minimal reproducible example. This simply determines how many runs in total your randomized search will try Remember, this is not grid search In parameters, you give what distributions your parameters will be sampled from

But you need one more setting to tell the function how many runs it will try in total, before concluding the search I hope i got the question right It depends on the ml model For example, consider the following code example

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