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Questions tagged [hyperparameter-tuning]

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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
24 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
40 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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30 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
63 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
32 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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3answers
61 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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0answers
15 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
21 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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2answers
84 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
34 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
23 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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38 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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21 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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0answers
31 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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0answers
13 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
50 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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1answer
16 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
48 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
75 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
27 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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2answers
83 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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0answers
10 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
777 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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4answers
208 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
215 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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0answers
29 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
144 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
98 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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0answers
108 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
75 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
25 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
725 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 ...
3
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1answer
58 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
26 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
7k 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
584 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
133 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 ...
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3answers
135 views

Hyperparameter Optimization for a Machine Learning Algorithm

I have a question regarding Hyperparameter Optimization for a Machine Learning Algorithm. I try to fit a Support Vector Classifier and use Hyperparameter-Tuning (but it could be also another ...
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4answers
223 views

Is it better to optimize hyperparameters or run multiple epochs?

Whenever I train a neural network I only have it go through a few epochs ( 1 to 3). This is because I am training them on a bad CPU and it would take some time to have the neural network go though ...
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0answers
23 views

What to do when two algorithms perform better on different set of classes while doing multi-class classification?

If an algorithm X performed better on a set of classes U and another algorithm Y works better on set of classes V where U and V don's share any class, what improvements can be done from a data ...
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26 views

Difference between MOE and Spearmint?

MOE seems to be a plain Bayesian optimization. Just curious if anyone knows about the difference.
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1answer
87 views

Hyper parameters and ValidationSet

Please correct me if I am wrong. "Training Set is used for calculating parameters of a machine learning model, Validation data is used for calculating hyperparameters of the same model (we use same ...
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2answers
5k views

Which parameters are hyper parameters in a linear regression?

Can the number of features used in a linear regression be regarded as a hyperparameter? Perhaps the choice of features?
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0answers
38 views

How does one interpret output from hyper-engine while optimizing a single hyperparameter of a neural network?

I am currently trying to optimize the learning rate of a neural network built in tensorflow. The network has 3 hidden layers, with 500, 250 and 100 neurons respectively. I have adapted code from an ...
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1answer
344 views

Is there any alternative to L-BFGS-B algorithm for hyperparameter optimization in Scikit learn?

The Gaussian process regression can be computed in scikit learn using an object of class GaussianProcessRegressor as: ...
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1answer
704 views

Optimising Kernel parameters using training data in GaussianProcessRegressor of Scikit-learn

I want to optimize the Kernel parameters or hyper-parameters using my training data in GaussianProcessRegressor of Scikit-learn.Following is my query: My training datasets are: X: 2-D Cartesian ...
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1answer
389 views

trying to decrease overfitting with regularisation in CNN

I am doing transfer learning by retraining the publicly available inception layer, without regularisation here are my initial parameters and results: ...
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1answer
46 views

tuning a convolution neural net, sample size

I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase? For example, ...
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0answers
141 views

How many epochs to run during hyperparameter search?

If I'm doing a hyperparameter search and comparing two different hyperparameters (but not number of epochs), is there some established rule of thumb for how many epochs to run? If I just compare ...