Questions tagged [hyperparameter-tuning]

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7 views

Hyperopt Model runs with 0 seconds duration

I use Hyperopt for Random Forest Regression hyperparameter tuning. my parameterspace is : ...
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17 views

How to put KerasClassifier, Hyperopt and Sklearn cross-validation together

I am performing a hyperparameter tuning optimization (hyperopt) tasks with sklearn on a Keras models. I am trying to optimize KerasClassifiers using the Sklearn cross-validation, Some code follows: <...
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16 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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1answer
11 views

CNNs - Hyperparameter tuning with different training sizes of the same data set

I would like to compare how much the classification performance (test accuracy) of CNNs changes depending on the size of the data set. For this I would like to use a data set like MNIST or Fashion ...
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25 views

How to tune the parameters of ANN in R?

I tried below code where I used method as 'mxnet': classifier = train(form = Survived ~ ., data = training_set_scaled, method = 'mxnet') From this code I got ...
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3answers
142 views

Why my network needs so many epochs to learn?

I'm working on a relation classification task for natural language processing and I have some questions about the learning process. I implemented a convolutional neural network using PyTorch, and I'm ...
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20 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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2answers
38 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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31 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 ...
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61 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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20 views

Fast way to tune SVM parameters in R

I have tried to tuned the parameters with the function tune, but it took over 15 hours to tune this (and at that point I had to stop it): ...
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1answer
72 views

Hyper parameters tuning XGBClassifier

I am working on a highly imbalanced dataset for a competition. The training data shape is : (166573, 14) ...
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1answer
42 views

When to use BayesianSearchCV and how it works?

Can somebody highlight when to use BayesianSearchCV and how it works? I have seen the implementation of same on kaggle and wanted to explore it further. Below is the link where the implementation ...
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1answer
50 views

Accuracy improving but, val_acc oscillating in ConvNet. What does it mean?

In my ConvNet model, i'm trying to classify some images. It is malware images and it doesn't contain complex features (i think), as expected model learn to classify images easily. You can see my ...
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1answer
44 views

Can `k=1` be a good choice for K neighbors classification?

Running sklearn.KNeighborsClassifier() on Kaggle's Leaf Classification sample (set of 99 species, 10 specimen each), with defaults kNN parameters and a grid search ...
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222 views

XGBOOST (sklearn interface) REGRESSION error

I am trying to run a GRIDSEARCHCV (sklearn) on XGBRegressor. Documentation on the parameter says that if regression, then objective = reg:squarederror.(see https://...
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2answers
95 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 ...
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1answer
132 views

What happens if GBM parameters (e.g., learning rate) vary as the training progresses?

In neural networks there is an idea of a "learning rate schedule" which changes the learning rate as training progresses. This made me ask the question, what would be the impact of varying ...
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66 views

Scaling neural networks

While using Neural Networks (TensorFlow: Deep Neural Regressor), when increasing your training data size from a sample to the whole data (say a 10x larger dataset), what changes should you make to the ...
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20 views

Hyperparameter Tuning with Simulated Data

I'm trying to create a SVM classifier which can predict some fault, and to train it I'm using simulated examples of the fault. Of course, the simulations are not perfect, but they appear to be good ...
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1answer
48 views

review: gradient descent, epochs, validation in neural network training

These days, training data aren't put in gradient descent all at once. Rather, they are put in batch after batch. Gradient descent is run once for each batch of training data. When all batches are ...
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184 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 ...
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1answer
53 views

How to decide how many n_neighbors to consider while implementing LocalOutlierFactor?

I have a data set with rows: 134000 and columns: 200. I am trying to identify the outliers in data set using LocalOutlierFactor from scikit-learn. Although I ...
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1answer
28 views

what exactly happens during each epoch in neural network training

Across different epochs, which of the following is/are updated? initial weights (initial ConvNet filter matrices, initial fully connected weights) hyper parameters: number of ConvNet filters, size ...
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52 views

How can I recognise if I can improve a random forest model by adding features

I want to tune a random forest model with caret package. I'm tuning it with cross-validation to prevent overfitting and resulted cross-validation accuracy is very ...
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36 views

Cross-validation and out-of-bag bootstrap applications

I have a question regarding steps on which a specific resample method should be used in general. As far as I know: out-of-bag bootstrap is the resample method with replacement, which has lower ...
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47 views

Hyperparameter optimization when calculating learning curves

I'm selecting a model for a regression problem and want to calculate learning curves. My dataset consists of ~20,000 x-y pairs. I'm using kernel ridge regression with different kernels, different ...
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18 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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2answers
55 views

Disadvantages of hyperparameter tuning on a random sample of dataset

I often work with very large datasets where it would be impractical to check all relevant combinations of hyperparameters when constructing a machine learning model. I'm considering randomly sampling ...
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27 views

Bayesian optimisation in deeplearning

Has anyone tried using Bayesian optimisation to get best learning rates, and other hyperparameters for deeplearning. How to change the parameters between the training. Any examples on callbacks? Can ...
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1answer
78 views

Is it necessary to tune the step size, when using Adam?

The Adam optimizer has four main hyperparameters. For example, looking at the Keras interface, we have: ...
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1answer
122 views

Hyperas LSTM configuration assignment error

I have been working on my trivial keras lstm model trying to implement Hyperas with the following code that gives me an error I cannot resolve. I have just been experimenting around with Hyperas and ...
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1answer
32 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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133 views

Validation accuracy is always close to training accuracy

I am trying to tune the hyperparameters of a LSTM I have to do time series forecasting. I have noticed that my validation accuracy is always very close to my training accuracy. I am not sure whether ...
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12 views

Hyper-parameter tuning when you don't have an access to the test data

I'm building models for SQUAD (Stanford Question Answering) dataset (https://rajpurkar.github.io/SQuAD-explorer). Stanford doesn't release its test set. It only provides us with training and dev ...
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2answers
1k views

What makes a Tree-Structured Parzen Estimator “tree-structured?”

From what I understand the Tree-Structured Parzen Estimator (TPE) creates two probability models based on hyperparameters that exceed the performance of some threshold and hyperparameters that don't. ...
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419 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 ...
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1answer
336 views

Hyperparameter tuning for stacked models

I'm reading the following kaggle post for learning how to incorporate model stacking http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/ in ML models. The structure ...
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49 views

std::bad_alloc with densenet and hyperas

I am filing this issue after being stagnated here for couple of weeks. I am using hyperas to find the hyperparameters for my network, Densenet. My issue here is that my evaluation always fails with ...
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1answer
177 views

Free parameters in logistic regression

When applying logistic regression, one is essentially applying the following function $1/(1 + e^{\beta x})$ to provide a decision boundary, where $\beta$ are a set of parameters that are learned by ...
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1answer
157 views

Tuning svm and cart hyperparameters

I am trying to optimize the hyperparameters of SVM and CART with tune() function of e1071 R package, but I have a doubt. Should I tune the parameters on the training data, fit the model on the ...
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161 views

What are the reasons of select a optimizer to be SGD or Adam in DQN?Why?

I saw several comparison between SGD, RMSPROP and ADAM but what I am looking for is their comparsion in DQN algorithm? What is best to select as optimizer SGD or Adam in DQN? Why? Please check the ...
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1answer
93 views

What is difference between final episodes of training and test in DQN?

What is difference between running in final episode of training mode and running in test mode in DQN? Is there any difference more than after training and tune the hyper-parameters, we test for one ...
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2answers
32 views

A way to Identify tuning parameters and their possible range

I am a novice in Machine Learning. But when I started learning, I figure out that all the methods have some tuning parameters and those parameters take a range of possible values. By grid searching, ...
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2answers
975 views

overfit a Random Forest

I am trying to overfit to the maximum a random forest classifier using scikit-learn to make some tests. Does somebody know what hyperparameters I can tune to do that? Or does somebody know which ...
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1answer
63 views

How to make it possible for a neural network to tune its own hyper parameters?

I am curious about what would happen to hyperparameters when they would be set by a neural network itself or by creating a neural network that encapsulates and influences the hyperparameters of the ...
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1answer
33 views

Where can we find the application of bayes's theorem in Bayesian optimiation with gaussian processing

I am trying to learn bayesian optimisation by following this tutorial. However, until now I don't get the relation between bayes's theorem to the gaussian process formalism. Any ideas?
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1answer
11k views

How to adjust the hyperparameters of MLP classifier to get more perfect performance

I am just getting touch with Multi-layer Perceptron. And, I got this accuracy when classifying the DEAP data with MLP. However, I have no idea how to adjust the hyperparameters for improving the ...
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2answers
1k views

How to choose the random seed?

I understand this question can be strang, but how do I pick the final random_seed for my classifier? Below is an example code. It uses the ...
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1answer
143 views

Benefits of using Deep Learning-specific hyperparameter optimization tools vs. sklearn?

There are quite a few library for hyperparameter optimization that are specific to Keras or other Deep Learning libraries, like Hyperas or Talos. My question is, what's the main benefit of using ...