Questions tagged [hyperparameter]

Hyperparameters of a model are the kind of parameters that cannot be directly learned during training but are set beforehand. Hyperparameters can define, for example, the complexity of the model or its capacity to learn.

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1answer
132 views

ROC AUC score is much less than average cross validation score

Using Lending club Dataset to find the propability of default. I am using hyperopt library to fine tune hyper parameter for an XGBclassifier and trying to maximize the ROC AUC score. I am also using ...
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1answer
14 views

Do i need to use hyperparamters from Gridsearch to train on WHOLE training set to get final model?

I just want to make sure i am on the right lines so please correct me if wrong. I am testing which hyperparmets are best for logisitic regession on my data X, y where X is featrues and y is target. X, ...
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1answer
24 views

How can I tune LSTM hyperparameters?

If anyone is there to answer these, that'll be great. I'm in the midst of a Final Year Project on LSTM. Currently, I’m stuck and confused over LSTM codes. There are 4 hyperparameters that I can play ...
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1answer
40 views

Search for hyperparameters whith different features using Random Forest

I have a dataset in which I would like to perform a classification model, so I have decided to use Random Forest. The number of features that I have is approximately 200 and I would like to test which ...
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0answers
26 views

Validation curve/RandomizedSearchCV difference train and test score

Ive build a RF model for an imbalanced data set that after feature selection has an F1 score of 54.26%. I am now trying to do hyper parameter tuning using RandomizedSearchCV, after creating validation ...
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24 views

Parameter optimization and selection in dynamic neural networks

I have used a Bayesian optimization to tune machine learning parameters. The optimized parameters are "Hidden layer size" and "...
2
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1answer
50 views

Hyperparameter optimization performance comparison

I have used Bayesian optimization for hyperparameter tuning in a machine learning model. What is the best way to compare the performance of network with and without Bayesian optimization? I found some ...
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0answers
34 views

Why do BERT classification do worse with longer sequence length?

I've been experimenting using transformer networks like BERT for some simple classification tasks. My tasks are binary assignment, the datasets are relatively balanced, and the corpus are abstracts ...
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2answers
72 views

Why SVM gridsearch takes longer time?

I have a dataset of 5K records and 60 features focussed on binary classification. Please find my code below for SVM paramter tuning. It's running for a longer time than ...
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33 views

Is copying parameter considered as plagarism?

So my friends and i are writing a kaggle assignment and the base code is written by me. One of my friend use my base code(feature engineering, labeling, etc.) and put it into a loop to find the best ...
2
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2answers
58 views

Hyperparameter optimization, ensembling instead of selecting with CV criteria

While burning CPUs performing a CV selection on a thin grid put on some hyperparameter space. I am using the `scikit-learn' API, for which the end result is a single point on the hyperparameter space, ...
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1answer
30 views

ML in R (caret-package) missing hyperparameters

I have a pretty specific question regarding the caret package however I still hope to finde help here. I recently worked with the caret package and trained a multilayer perceptron with ...
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15 views

Track validation_curve during hyperparameter optimization

To study the influence of a single (hyper-)parameter, I use validation_curve: ...
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2answers
38 views

is learning rate schedule a hyperparameter? [duplicate]

I believe term ‘learning rate schedule’ is a certain solution for tuning the learning rate. But at the same time, every parameter evaluating the parameter itself can be called a hyperparameter. So can ...
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1answer
161 views

Grid search or gradient descent?

Assume we have a neural network and one if its activation functions is a function of parameter a. We want to find the weights and parameter a that leads to the minimum loss on the validation set which ...
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1answer
34 views

Difference between validation and prediction

As a follow-up to Validate via predict() or via fit()? I wonder about the difference between validation and prediction. To keep it simple, I will refer to train, <...
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1answer
80 views

Hyperparameter tuning and cross validation

I have some confusion about proper usage of cross-validation to tune hyperparameters and evaluate estimator performance and generalizeability. As I understand it, this would be the process you ...
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1answer
30 views

Similarity of XGBoost models?

Is xgboost with n_estimators = 100 and learning_rate = 0.1, same as xgboost with n_estimators = 50 and learning_rate = 0.2 ?
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1answer
28 views

Accuracy noise patterns during model training

I'm training a logistic regression model on a small dataset. I have about 1300 samples that I split into a training and a testing set (70% and 30% respectively). The training seems ok, however when I ...
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1answer
176 views

How to choose the model parameters (RandomizedSearchCV, .GridSearchCV) or manually

Faced with the task of selecting parameters for the lightgbm model, the question accordingly arises, what is the best way to select them? I used the RandomizedSearchCV method, within 10 hours the ...
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0answers
19 views

Analyzing the search space of hyperparameter optimization

My goal is to train a CNN via transfer learning on a given dataset and to analyze and document the training process. I selected a few CNN architectures and hyperparameters to perform a random search. ...
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0answers
12 views

Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
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1answer
44 views

How to optimize hyperparameters in stacked model?

I was wondering whether somebody could explain how to optimize hyperparameters for the base learners and meta algorithm when stacking? In many tutorials they seem to be plucked out of thin air! ...
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0answers
35 views

Hyperopt Model runs with 0 seconds duration

I use Hyperopt for Random Forest Regression hyperparameter tuning. my parameterspace is : ...
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2answers
40 views

Parallel hyperparameter optimization techniques?

Most hyperparameter optimization technique want to evaluate points one by one. I have an expensive optimization problem, but i can run hundreds of evaluations in parallel. The dimension of the problem ...
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1answer
114 views

KNN RandomizedSearchCV typerror

While trying to study a binary classification problem with KNN and trying to tune the parameters of the model I'm getting a typerror that I quite don't understand. Is a parameter missing or something? ...
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170 views

Error while trying to do hyperparameter tuning using hyperas

I am getting a syntax error while using hyperas and am not sure why. My code: ...
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17 views

Slowly decreasing validation / training cost and their abnormal values

I have a dataset of size ~100,000 of images, I'm training a CNN model on them for regression. optimizer: Adam batch_size: 64 Number of epochs: 50 When I set the ...
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0answers
42 views

Confused about hyper-parameter search and network architecture search in NAS

I want to understand automatic Neural Architecture Search (NAS). I read already multiple papers, but I cannot figure out what the actual search space of NAS is / how are classical hyper-parameters ...
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1answer
15 views

A doubt about the GridSearchCV function in Sklearn?

When creating different hyperparameter combinations, does the function evaluate combination 1 on the same fold as combination 2? As in, are the folds the same across combinations? I understand that ...
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2answers
81 views

Is it a good idea to tune the number of folds for cross validation when tuning hyperparameters of RF

I'm new to data science. I'm trying to get the best model for Random Forest. Unfortunately, I'm not sure if my idea can produce a good generalized model. 1) I have split data to TrainingSet (70%) and ...
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0answers
54 views

Has anybody used alternative hyperparameter optimization techniques (other than default one) in SK-Learn?

I've been using Sklearn for Gaussian process regression that has L-BFGS-B (“fmin_l_bfgs_b”) as a default optimization algorithm. I want to implement some other ...
3
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1answer
137 views

Hyperopt vs Default Values

When I use the hyperopt library to tune my Random Forest classifier, I get the following results: Hyperopt estimated optimum {'max_depth': 10.0, 'n_estimators': 300.0} However, when I train the ...
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1answer
36 views

Generative network understanding

I was going through GAN's notebook by fchallot on Generative Adversarial Networks where, in the Generator Network, he creates a Dense layer with $16*16 * 128$ (...
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1answer
141 views

How to handle the parameter space of neural networks?

This question is very broad (and might even be closed as "too broad"). It can be considered as a beginners question, because it is largely about getting started in terms of heading into a direction ...
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0answers
561 views

Bayesian optimization for a Light GBM Model

I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choice) ...
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0answers
44 views

Reasonable hyperparameters for NuSVR?

I'm looking for reasonable hyperparameters grid for NuSVR. For now I have: ...
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1answer
111 views

Tuning C hyper parameter in Soft Margin SVM in Matlab

How to tune the C 'BoxConstraint' hyperparameter in soft margin SVM to get the best optimal value?
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175 views

SVM hard and soft margins in matlab,

I am comparing the performances of several SVM models in matlab using the fitcsvm function, and I want to double check that I am using the correct syntax for hard ...
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0answers
172 views

SVM hyperparameters using Matlab's fitcsvm and OptimizeHyperparameters

I am building SVM models and will compare their performances, linear vs RBF, and I'm using OptimizeHyperparameters to get best hyperparameters C (BoxConstraints) ...
7
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1answer
265 views

Is it OK to try to find the best PCA k parameter as we do with other hyperparameters?

Principal Component Analysis (PCA) is used to reduce n-dimensional data to k-dimensional data to speed things up in machine learning. After PCA is applied, one can check how much of the variance of ...
2
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2answers
328 views

Setting best SVM hyper parameters

I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab ...
0
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1answer
174 views

What is the possible range of SVR parameters range?

I'm working on a regression problem. While tunning the Parameters of SVR I got the following values c=100, gamma= 10 and epsilon =100. For which I got 95 percent r-square. My question is what is the ...
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0answers
48 views

Problem about tuning hyper-parametres

I have tried GridSearchCV and BayesSearchCV for tuning my LightGBM algorithm (for binary classification). I have used 10 iterations and I have indicated scoring ="roc_auc" In the first iteration, I ...
9
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2answers
837 views

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 ...
3
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2answers
84 views

How to set hyperparameters in SVM classification

I am studying image classification using SVMs and it is generally defined as so... N = number of training examples W = is the weights f(x, W) = dot product λ is explained to be set through cross-...
4
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1answer
120 views

xgboost or lightgbm to handle Binomial problems

I have a dataset containing a column of trials, a column of successes and other features; and, obviously, I can generate a probability column. I would like to use gradient boosting methods (like ...
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0answers
20 views

Any heuristic for minimal DCGAN latent space dimension?

I am highly interested in approaching minimal latent space dimension (as many other may be) for DCGANs or autoencoders. In this example of DCGAN on the MNIST dataset, the person uses a 100-...
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1answer
45 views

hypeparameters tuning neural network according to loss vs according to scoring function

During hyperparameters tuning we select a metric to measure performance of the model. Example of metrics : f1 score, precision, recall, AUC ... In general, for the training of neural networks, back-...
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4answers
1k views

Which is first ? Tuning the parameters or selecting the model

I've been reading about how we split our data into 3 parts; generally, we use the validation set to help us tune the parameters and the test set to have an unbiased estimate on how well does our model ...