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21 views

Can we use MLE estimates as hyperparameters of bayesian linear regression?

Given a linear regression \begin{align} y_i = \mathbf{x}_i^T \mathbf{b} \qquad i = 1,..,N \end{align} or in matricial form: \begin{align} \mathbf{y} = \mathbf{X}^T \mathbf{b} \end{align} MLE ...
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0answers
11 views

optimise sharpe ratio with caret package

I am trying to see if what I used to do manually can fit into the caret package framework. Given a set of potential signals (=features), I need to select a subset that optimises the out of sample ...
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1answer
26 views

Improvements of Random Search for Hyperparameter Optimization [closed]

Random search is one possibility for hyperparameter optimization in machine learning. I have applied random search to search for the best hyperparameters of a SVM classifier with a RBF kernel. ...
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0answers
5 views

Why is the parameter and the random variable swapped in this conjugate distribution pdf?

I'm reading a journal titled Claims reserving in the hierarchical generalized linear model (hglm) by Gigante, Picech and Sigalotti. In the distributional assumption for the unobserved risk parameters, ...
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0answers
10 views

What distribution for randomized search in hyperparameter estimation?

I'm trying to find the optimal hyperparameters, so I'm using RandomizedSearchCV on sciki-learn. One of the parameters of this method is ...
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0answers
26 views

How do I optimize a bioinformatics pipeline for novel data sets?

I'm putting the finishing touches on a bioinformatics pipeline for omics data. There are many sequential interlocking parts (e.g. model fitting, regression, classification, clustering, etc). The final ...
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0answers
17 views

How to sample weights for weighted kernels?

I'm using a SVM classifier with a weighted RBF kernel. My dataset has 17 features. In the RBF kernel I will use a weight for each feature. Of course the weights must sum to one. For choosing the best ...
1
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1answer
29 views

Parameter selection and k-fold cross validation

I have one dataset, and need to do cross-validation, for example, a 10-fold cross-validation, on the entire dataset. I would like to use radial basis function (RBF) kernel with parameter selection ...
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3answers
70 views

Hyperparameter optimization with random search

I would like to do a random search for hyperparameter optimization. The procedure can be found in link. One possibility is to define a fine grid and take random combinations. A better approach would ...
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1answer
26 views

hyper parameter optimization grid search issues

I keep running into the same problem while doing a grid search to optimize the C and gamma parameters of an SVC. Every time i do the grid search, the best values seem to occur at around C = 100000 ...
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5answers
793 views

What's in a name: hyperparameters

So in a normal distribution, we have two parameters: mean $\mu$ and variance $\sigma^2$. In the book Pattern Recognition and Machine Learning, there suddenly appears a hyperparameter $\lambda$ in the ...
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0answers
36 views

Tuning Parameters for Boosting/Bagging/Random Forest

I want to use tree-based classifiers for my classifiaction problem. I'm thinking about bagging, boosting (AdaBoost, LogitBoost, RUSBoost) and Random Forest but I'm unsure about the tuning parameters, ...
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1answer
45 views

Does memory ever really matter for mini-batch size selection?

I'm new to machine learning, and am confused about some aspects of stochastic gradient decent. I've read in several places that, when using vectorized code, the reason that mini-batching in ...
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0answers
12 views

How is it correct to optimize a binary classifier output threshold with ROC and LPOCV?

Hello everyone and thank you in advance for you help! I'm building a screening tool with a machine learning algorithm. The model provides a probabilistic prediction (i.e. logistic regression, ...
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0answers
38 views

Neural Network: How are number of parameters calculated?

I have a question about how the number of parameters (and limit for now to the number of weights because there can be other parameters as well) is calculated. Consider the screenshot below from the ...
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0answers
17 views

Hyperparameter optimization on large dataset

I have a huge dataset and want to carry out regression, such as gradient boosting. The problem is that the dataset is huge and hyperparameter optimization is computational expensive, especially I use ...
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0answers
40 views

Prior distributions for Alpha and beta parameters of beta-binomial model

Say that I'm constructing a beta-binomial model. The data comes from a binomial distribution, I assume that the parameter came from a beta distribution (my prior belief) - yet I've noticed that the ...
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0answers
22 views

How likely is a hyperparameter search on a subset dataset going to be accurate on the full dataset?

I'm running a hyper parameter search over many options in a neural network, holding all but one fixed in turn until I come to rest on a reasonably optimal set of hyperparameters. I wonder if anyone ...
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1answer
20 views

Why does the Hyperparameter optimization method GridSearch suffer from the curse of dimensionality?

An example accompanied by explanation is needed since I am a complete noob in these area.
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0answers
9 views

Heuristically, to what extent do estimator's hyperparameter tunning influence results?

Let's supose that you: Want to take a first insight into a predictive problem (classification, regression, clustering etc.). Don't need the optimal solution, just get an idea of how good the ...
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1answer
31 views

Can different classification methods be compared in the same manner as models during hyper-parameter tuning?

If I would like to choose between different classifiers, e.g. support vector machines (SVM) and boosted trees, based on their generalization performance, can I do this in the same way as I would do ...
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0answers
5 views

What does “Clamping factor” stands for in unsupervised label propogation?

see for example in sklearn package: http://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html see reference http://pages.ucsd.edu/~ztu/publication/iccv13_dlp.pdf ...
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0answers
32 views

Why is there a perceived difference between a Bayesian Hyperparameter and a Machine Learning Hyperparameter?

There are two seperate Wikipedia articles for the term Hyperparameter. One for Bayesian statistics and another for machine learning methods. Why is this so? BOTH of these definitions imply that ...
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0answers
15 views

deriving shrinkage factors for beta binomial distributions

I am confused by the derivation of shrinkage factors currently online on sites like Wikipedia and ProbWiki. To be on point, I am confused how we get quickly from $\theta_i \sim Beta(\mu,M)$ to ...
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0answers
24 views

How high is “high” and how low is “low” in Latent Dirichlet Allocation Alpha and Eta hyperparameters? - LDA

In relation to this question and answer, the default value for the Python LDA for alpha is 0.1 and ...
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1answer
137 views

Denoising Autoencoder not training properly

I've implemented a denoising autoencoder using TensorFlow. The code is here, there is also a command line script to launch it. The code seems to work, the cross-validation error is decreasing every ...
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1answer
124 views

Random forest low score on testing data (scikit-learn)

I am trying to train my model using Scikit-learn's Random forest (Regression) and have tried to use GridSearch with Cross-validation (CV=5) to tune hyperparameters. I fixed ...
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1answer
259 views

Advantages of Particle Swarm Optimization over Bayesian Optimization for hyperparameter tuning?

There's substantial contemporary research on Bayesian Optimization (1) for tuning ML hyperparameters. The driving motivation here is that a minimal number of data points are required to make informed ...
2
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1answer
30 views

Levels of “hyperparameterization” in Hierarchical Modeling

Suppose we have observations $y$ that we wish to model as having being randomly sampled from a distribution with parameter $\theta$. General Bayesian approach assumes a prior distribution over ...
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0answers
42 views

How could a hyperparameter grid search be visualised?

Consider a hyperparameter grid search that looks at the training and testing scores of an estimator with respect to multiple parameters like training epochs, number of nodes in layer 1, number of ...
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0answers
86 views

normalization to zero mean and variance one logistic regression & random forrests

i was just thinking how does normalization to 0 mean and variance 1 (using an affine linear mapping) can impact the classification accuracy and the choice of hyperparameters when using: logistic ...
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0answers
27 views

Different Loss Functions used when fitting the model and tuning hyperparameters with cross validation

I usually do a two step process to fit a model: Given a hyper-parameter, fit the model using some criterion, such as MLE, etc. Do a k-fold cross validation and try a different hyper-parameter on ...
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0answers
51 views

Is this wikipedia article about KNN contradicting itself regarding “non-parametric”?

I understood that KNN (K-Nearest-Neighbors) was non-parametric, after reading the beginning of the wikipedia article here: ...
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1answer
123 views

How to determine the number of iterations for Latent Dirichlet Allocation

I am performing Latent Dirichlet Allocation for 240 test documents (trained model with 3361 documents). I am using 150 iterations and 120 burn in iterations. Is there a specific way to determine ...
2
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0answers
531 views

How to use XGboost.cv with hyperparameters optimization?

I want to optimize hyperparameters of XGboost using crossvalidation. However, it is not clear how to obtain the model from xgb.cv. For instance I call ...
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2answers
103 views

How exactly does one marginalize over parameters in an N-dimensional likelihood?

I see no equations for the following, so I'm not sure exactly what they are talking about: "For each model, we determine the best fit parameters from the peak of the N-dimensional likelihood surface. ...
1
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1answer
36 views

Can I use cross validation on a subset of the training set to select hyperparameters?

I am using R, and I had a dataset with 400000 rows and 800 columns, training a random forest model with only 100 trees on this dataset will take me about 1 and half hour on my laptop. So I went on and ...
2
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1answer
42 views

“Continuity” of SVM as a function of hyperparameters

Suppose we have some (large enough) labeled training set and use some exhaustive cross-validation technique (e.g. leave one out) for tuning hyperparameters of SVM (with some nonlinear kernel). Is it ...
2
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1answer
120 views

Can I do hyper-parameters optimization before model selection?

For every N model: Split in test and train subsets(Using the same seed for every N model) Randomized Search of parameters with 5 k-folds on train subset Select the best estimator obtained after the ...
2
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1answer
99 views

Hyperparameters Optimisation in Gaussian process for regression

I am trying to perform Gaussian Process for regression. I chose the SE Kernel : $K(x_i,x_j) = \exp(-\frac{||x_i-x_j||^2}{l}) + \sigma_n\delta_{i,j}$. I begin by maximize the log-likelihood with ...
0
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1answer
80 views

Which is better, MSE or classification rate, for minimizing the likelihood of overfitting?

During the process of selecting a classification model (with cross validation), if one uses MSE of the predicted posterior probabilities as a selection criterion, then a model ...
3
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2answers
208 views

Visualization figures for the grid search optimization steps?

I am using grid search in order to find the best values for the SVM parameters (namely C and ...
1
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1answer
244 views

How to optimize RBF parameters $C,\gamma$ with KSVM method?

I want to find the best choice of $C$ and $\gamma$ parameters for Radial Basis Function kernel. I am using kernlab instead of e1071 library. So how can i optimize RBF parameters $C$ and $\gamma$ with ...
2
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3answers
252 views

Practical hyperparameter optimization: Random vs. grid search

I'm currently going through Bengio's and Bergsta's Random Search for Hyper-Parameter Optimization [1] where the authors claim random search is more efficient than grid search in achieving ...
0
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0answers
100 views

marginal likelihood in linear bayesian regression (in weight-space)

I want to tune the hyperparameters namely the target deviance $\sigma_y$ and weight deviance $\sigma_w$ in bayesian linear regression. The posterior distribution in level-1 inference which is ...
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1answer
1k views

Hyperparameter Tuning - What is possible in terms of accuracy gain?

A question from a newbie: I played around with parameter tuning (grid, random search) in R (caret, xgboost) and my observation is as follows: in terms of accuracy gains I was able to get 3 - 7% but ...
2
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2answers
436 views

Nested cross-validation - how is it different from model selection via kfold CV on the training set?

I often see people talking about 5x2 cross-validation as a special case of nested cross validation. I assume the first number (here: 5) refers to the number of folds in the inner loop and the second ...
0
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0answers
30 views

set SVM parameter range values for tuning [duplicate]

I am newbie to using svm for classification. I want to tune svm parameters by .TrainAutofunction in EmguCV. But I don't know what are the range(min-max value) of below parameters that I should give to ...
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1answer
533 views

What do we mean by hyperparameters? [duplicate]

Can anyone give me full details about what we mean by hyperparameters, and what in the Dirichlet distribution are called hyperparameters? A practice example for the estimation of those parameters ...
0
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1answer
43 views

Marginal Likelihood of a Gaussian Process Model, Duplicate entries in kernel matrix

I am trying to fit a Gaussian process model using the toolbox and I got stuck in the following problem. Assuming that I have some duplicated data points in my training data, then those will map to ...