All Questions
7 questions
1vote
1answer
45views
RFECV and grid search - what sets to use for hyperparameter tuning?
I am running machine learning models (all with sci-kit learn estimators, no neural networks) using a custom dataset with a number of features and binomial output. I first split the dataset into 0.6 (...
- 11
0votes
0answers
271views
Correct method to report Randomized Search CV results
I have searched online but I still cannot find a definitive answer on how to "correctly" report the results from hyperparameter tuning a machine learning model; though, this may just be some ...
- 173
0votes
1answer
377views
why sign flip to indicate loss in hyperopt? [closed]
I am using the hyperopt to find best hyperparameters for Random forest. My objective is to get the parameters which returns the best f1-score as my dataset is ...
- 2,715
0votes
2answers
974views
GridSearch on imbalanced datasets
Im trying to use gridsearch to find the best parameter for my model. Knowing that I have to implement nearmiss undersampling method while doing cross validation, should I fit my gridsearch on my ...
- 135
0votes
1answer
1kviews
Does GridSearchCV not save the best parameters?
So I tuned the hyperparameters using GridSearchCV, fitted the model to the data, and then used best_params_. I'm just curious ...
- 3
2votes
3answers
23kviews
Hyper-parameter tuning of NaiveBayes Classier
I'm fairly new to machine learning and I'm aware of the concept of hyper-parameters tuning of classifiers, and I've come across a couple of examples of this technique. However, I'm trying to use ...
- 141
1vote
0answers
49views
Minimizing overfitting when doing hyperparameter Tuning
Generaly when using Sklearn's GridSearchCV (or RandomizedGridSearchCV), we get best model with best test score even if the model overfits a little bit. How can we compute generalization error ...
