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Tagged with randomized-algorithmshyperparameter-tuning
5 questions
0votes
1answer
680views
Is it mandatory to set a random_state when using RandomizedSearchCV?
When I use RandomizedSearchCV, if I put the random state I always obtain the same results with the same hyperparams trainer. So, is it mandatory to use? Because in my opinion it is better to always ...
0votes
1answer
515views
What is the objective that is optimized with Random Search?
I have recently learned about Random Search (or sklearn.model_selection.RandomizedSearchCV in Python) and was thinking about the theory behind the optimization process. In particular my question is, ...
3votes
3answers
4kviews
Cannot clone object <keras.wrappers.scikit_learn.KerasRegressor object at 0x7fdc9c3ba550>
Trying to hypertune ANN but getting an error while using fit..(grid1.fit(X_train, y_train)) Below is the code ...
11votes
2answers
2kviews
What is the most efficient method for hyperparameter optimization in scikit-learn?
An overview of the hyperparameter optimization process in scikit-learn is here. Exhaustive grid search will find the optimal set of hyperparameters for a model. The downside is that exhaustive grid ...
- 22.9k
5votes
2answers
7kviews
How to choose the random seed?
I understand this question can be strange, but how do I pick the final random_seed for my classifier? Below is an example code. It uses the ...
- 3,618
