Questions tagged [tuning]
The tuning tag has no usage guidance.
22
questions
2
votes
0answers
21 views
High dimensional hyperparameter tune
Many already known optimization techniques rely on past data (Bayesian optimization for instance) and perform really well for a bunch of hyperparameters. Is there, however, a good tuner/tuning method ...
0
votes
0answers
10 views
Multimetric hyperparameter selection
I am solving a supervised learning classification problem, and since I'm using xgboost I've done an optimal hyperparameter search by randomly choosing hyperparameters in a K-fold CV setting.
My ...
0
votes
0answers
8 views
Is there a principled method for preventing overfitting of a model to the validation set?
Overfitting almost always implicitly refers to overfitting onto the training set, which could occur, for instance, when a model is trained for too long, where we see a dip in the validation accuracy ...
0
votes
0answers
12 views
Tuning with a lower early stopping to find relationships between hyperparameters
I have a neural network with the common hyperparameters to optimize. For example, the regularization term (lambda) and the number of hidden units, as well as the number of early stopping rounds.
In ...
0
votes
0answers
8 views
relationship between complexity of hyperparameter tuning algorithm and the performance of a machine learning model?
Is there any paper/references that talks about the relationship between complexity of hyperparameter tuning algorithm and the overall performance of a machine learning model (e.g. the performance of a ...
0
votes
0answers
22 views
LASSO: Its derivative
I am trying to find the optimal tuning parameter ($\lambda$) for my variable selection problem with LASSO penalty.
My optimization problem is:
$$Q(\beta)=-l(\beta)+\lambda\sum_{j=1}^d |\beta_j| $$
In ...
0
votes
0answers
14 views
What is the difference between hyperparameter tuning and neural architecture search?
I am not very clear about the difference between hyperparameter tuning and neural architecture search (NAS).
Recently year there has been a lot of these so-called NAS algorithms, such as DARTS. ...
0
votes
1answer
15 views
Hyperparameter tuning vs weight tweaking in Cross-Validation: should I consider 2 different validation sets?
Let's say I have 1000 Samples and want to build an ANN. Then I split my dataset into train set (800) and test set (200). After that, I do the following
Cross-validate my train set with different ...
0
votes
1answer
15 views
Cross-validation for hyperparameter tuning
I've read as many topics regarding hyperparameter tuning as I could, and I developed the following algorithm for hyperparameter tuning & final model building
Split the data in train set (80%) &...
0
votes
0answers
10 views
Burn-in iterations vs Tuning iterations of MCMC
I am running an MCMC that requires as parameters a number of burn-in iterations and a number of tuning iterations. I understand that burn-in iterations are discarded from the posterior sample, but ...
1
vote
0answers
23 views
How can one use Grid Search without overfitting the model?
I checked several questions, like Overfitting during model selection - AutoML vs Grid search and Hyperparameter tuning using grid search/randomised search, but I don't think any of them answer my ...
6
votes
1answer
74 views
Is there a hard distinction between hyperparameter vs parameter in machine learning?
I was watching Andrew Ng's lecture on the difference between parameter vs hyperparameter, https://www.youtube.com/watch?v=VTE2KlfoO3Q&ab_channel=Deeplearning.ai, and a question came to me.
Is ...
0
votes
0answers
66 views
Proper way to incorporated CalibratedClassifierCV in cross-validation in Scikit
I'm creating some classifiers for a binary classification problem. I want to find out three things:
Which algorithm I should use.
Which set of hyperparameters I
should use.
If I should calibrate the ...
2
votes
2answers
55 views
Tuning hyperparameters never affects weights?
I am trying to better understand “tuning the hyperparameters”. I understand how to use GridSearchCV, I found the below explanation useful:
“As we do not know whether those parameters affect each other,...
0
votes
0answers
37 views
Best score in SVM
i am new to machine learning and i took the house price dataset from kaggle.com to learn and understand SVM.
for regression the best score would be 0.0 and for classification the
best score ...
0
votes
1answer
65 views
Perceptual Loss Layers Selection
I understand that in order to improve your generative model performance it is quite useful to compare your output and the target in the feature space, as stated in the paper Perceptual Losses for Real-...
1
vote
0answers
27 views
Is Hyperparameter Optimization A Discrete Or Continuous Problem?
I'm currently learning AI/machine-learning with Python and Scikit-learn. Not having a strong background in math, I'm confused on a certain point.
Say I want to tune the parameters of a machine ...
0
votes
1answer
79 views
how to do the hyper parameter tunning for one class svm in r programming?
x is input (single column)
tuned <- tune.svm(x=x, y =NULL, data=x,
type= 'one-classification',
tunecontrol = tune.control(sampling = "fix"))
For this I am ...
0
votes
0answers
15 views
Boosting Algorithm: What would happen if you omit the lambda (Make it equal 1 or make it too large) in this algorithm?
Taken from The Introduction to Statistical Learning textbook. I read the excerpt about boosting and have a fine conceptual understanding of the matter. Although I am curious why the learning parameter ...
0
votes
0answers
9 views
Caret for model performing and tuning paramter
I would like to ask, if I could use cross validation in caret for tuning parametr lambda and also for evaluating model performance. If I use savePredictions="final" and also I will tune parametr, it ...
0
votes
0answers
22 views
Tuning hyperparameters using sklearn GridSearchCV or validation_curve?
I'm working on tuning a classifier (so far just a decision tree) and running my classifier through both sklearn's GridSearchCV and validation_curve. Is either of these methods preferred and when would ...
3
votes
1answer
31 views
Model tuning in the presence of incorrect training labels
I have a situation where I have a large amount of labeled data (~40 million records) with a binary outcome variable that has about 50% positive and 50% negative cases. The issue is that I know that ...
