All Questions
Tagged with scikit-learnhyperparameter-tuning
48 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
1vote
0answers
102views
Confused about use of random states for training models in scikit
I am new to ML and currently working on improving the accuracy of an MLPClassifier in scikit. My code looks like so ...
- 25
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
1vote
1answer
64views
How are the successive sets of training samples that are allocated for each iteration of HalvingGridSearchCV determined?
The scikit-learn classes HalvingGridSearchCV and HalvingRandomSearchCV implement a hyperparameter tuning method known as successive halving. It is an iterative selection process in which all the ...
- 185
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
629views
Tuned model has higher CV accuracy, but a lower test accuracy. Should I use the tuned or untuned model?
I am working on a classification problem using Sci Kit Learn and am confused on how to properly tune hyper parameters to get the "best" model. Before any tuning, my logistic regression ...
1vote
1answer
755views
How to determine which combinations of parameters to include in GridSearchCV
I am using MLPClassifier from sklearn and I would like to tune it with GridSearchCV. But I don't know which set of values to include for hidden_layer_sizes, max_iter, activation, solver, etc. How can ...
- 11
0votes
1answer
1kviews
Is there any benefit to using cross validation from the XGBoost library over sklearn when tuning hyperparameters?
The XGBoost library has its own implementation of cross validation through xgboost.cv(). It looks like it requires data be stored as a DMatrix. Instead of using <...
- 101
2votes
2answers
3kviews
Grid_search (RandomizedSearchCV) extremely slow with SVM (SVC)
I'm testing hyperparameters for an SVM, however, when I resort to Gridsearch or RandomizedSearchCV, I haven't been able to get a resolution, because the processing time is exceeding hours. My dataset ...
2votes
3answers
660views
Is there a point in hyperparameter tuning for Random Forests?
I have a binary classification task with substantial class imbalance (99% negative - 1% positive). I want to developed a Random Forest model to make prediction, and after establishing a baseline (with ...
- 21
0votes
1answer
686views
What does a leaf size of 1 in K-neighbors regression mean?
I am doing hyperparameter tuning + cross validation and I'm constantly getting that the optimal size of the leaf should be 1. Should I worry? Is this a sign of overfitting?
- 119
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
1vote
1answer
1kviews
How to train multioutput classification with hyperparameter tuning in sklearn?
I am working on a simple multioutput classification problem and noticed this error showing up whenever running the below code: ...
- 299
2votes
1answer
58views
Find smooth global maximum from noisy points
Let's say I have a number of sampled data points and resulting values for each. In practice this may be a high dimensional problem, but here's a one dimensional example: In the above example, the ...
1vote
1answer
171views
Could I directly apply techniques for hyper-parameter tuning, and choose the best model?
I have noticed in some sources the author first trains the model (say a model from scikit-learn) with the default hyper-parameters, and the model naturally gives a ...
