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Questions tagged [train]

training (or estimation) of statistical models or algorithms.

87 questions with no upvoted or accepted answers
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votes
1answer
159 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
4
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0answers
856 views

Pre-training deep neural networks by supervised learning

When pre-training deep neural networks layer by layer, is it normal to pre-train the layers -which haven't been pre-trained by unsupervised training- by using supervised training before we train the ...
4
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2answers
110 views

Practical realities of updating a trained model with new data

In my day to day work, I train models on data using R packages that have no extension for Bayesian priors. I will generally have a large dataset to start off with, and add new data as needed. Any ...
3
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0answers
308 views

How to not overlook rare but important features when preventing over-fitting in a decision tree?

I have a data set where some binary features divide the sample space roughly in half, whereas other features are much less frequent and occur only for 0.0001 - 0.01 of the sample space. However, those ...
3
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0answers
1k views

HMM library, different length sequences training

I'm using the Kevin Murphy's HMM library in MATLAB(http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html) There is a section called 'How to use the toolbox'. There is this example for GMM ouputs: <...
2
votes
1answer
65 views

Data leakage when using walk forward optimization

I am setting up a neural network that will predict the incoming customers at a store for the next seven days (the output is a list with seven numbers, one for each day). As input, I will give the ...
2
votes
1answer
75 views

deep learning : how to retrain an already trained model on different data?

I have a model (a convolutional neural network) that has already been trained on set of training data for human pose estimation. It works very well for simple cases, but I want to make it more robust ...
2
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0answers
132 views

Reducing batch size after X epochs?

Batch size and the number of iterations are considered as a tradeoff. It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, ...
2
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0answers
22 views

Is there an official rule, or a generally accepted one, for how closely your validation data should match your training data?

I'm finding myself making a gut-feeling judgment more and more often on whether the validation of my model is "close enough" to the modeled results from my training data. I don't recall having ever ...
2
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0answers
445 views

Creating training and validation sets for churn model

I need to determine a statistically sound methodology for creating training and validation datasets for a churn model. Testing sets and model selection aren't a problem. The data spans 4 years of ...
2
votes
1answer
78 views

Classifying users in one of two groups

I've a - probably beginner level - question about classifying. My goal is to classify users as UG1 or UG2, based on some specific characteristics of them. So, what I've done so far is: I have two ...
2
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0answers
97 views

Machine learning: Training and Development Sets

There are Training Set, Development Set (to tune the parameters) Test Set. Is it possible to use SVM with the Training Set and various of features set, to get various of classifiers, and then test ...
2
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0answers
33 views

What is allowed on the test set?

This question considers the set-up of a data set, not learning on an existing data set. The data set we are setting up is comprised of two parts: train and test. The set as a whole (say, S) is a ...
2
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0answers
1k views

Are RSS and R^2 related to training error only?

While reading An Introduction to Statistical Learning, I stumbled across the following (p. 210): [...] the model containing all of the predictors will always have the smallest $RSS$ and the ...
2
votes
1answer
65 views

How to deal with frequencies that don't appear in the held-out set?

The held out probability is defined as $$P_{HO}\left(x\right)=\frac{t_{r}}{N_{r}\cdot\left|S^{H}\right|}$$ where: $t_{r}$ is the total number of times events that appeared $r$ times in the training ...
2
votes
1answer
134 views

Why do we have to be concerned about the problem of overfitting on the training set?

For a hypothesis set $H=\{h_1,...,h_M\}$, randomly sampled training set $D_{train}$, and a learned hypothesis $g$ using $D_{train}$, the VC-bound of a finite hypothesis set tells us $$ P[|E_{in}(g)-...
2
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0answers
953 views

Random Forest online/incremental learning in R

Is there a Random Forest implementation available in R, that supports online learning? My alternative approach was to use the popular randomForest package and combine Random Forests (the existing one ...
2
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0answers
1k views

Test MAPE < Train MAPE using auto.arima()

I am trying to build a forecasting model for the passenger vehicles registrations in a given country, and I wanted to use $auto.arima$ function from the $forecast$ package to estimate a simple ARIMA ...
2
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0answers
103 views

Expected required sample length to train a hidden Markov model

Say one wishes to train a hidden Markov model with $n$ hidden states, and (accidentally) the problem itself can be described with a hidden Markov model with $n$ (or less states). What is the expected ...
1
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0answers
22 views

how to select training and testing data for interpolation in 100 instances data?

I would like to divide my data of only 100 instances into training and testing an use the training data to fit a curve(interpolate) and use the testing data to calculate the error at the interpolated ...
1
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0answers
19 views

How do I check my GAN implementation is correct?

I wrote a GAN implementation and I trained that to produce some sample images after training on a dataset. The images looked visually fine. Now I want to test my implementation on the CI and make ...
1
vote
1answer
31 views

The performance metric used in prediction is different from the objective function to train the model

For linear regression and many machine learning models, we use the same performance metric during the training and testing stage. For example, during the training stage, our machine learning algorithm ...
1
vote
2answers
118 views

Data set splitting for statistical inference?

probably a very basic question -- I am modelling companies' decisions on which mode of payment they use in M&A deals with a help of logit model. I am so far interested only in what variables are ...
1
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0answers
482 views

Drawing ROC curves from RFE() training results in caret

I want to generate ROC curves using the training data and results from the rfe function in caret. I have managed to do this with the code below but there is some inconsistency between the ROC value ...
1
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0answers
186 views

I am trying to build a progressive auto encoder neural network and I am not sure how to discard old weights?

The goal of the network is simple, encode and decode images at a smaller scale and slowly increasing the network complexity, the input image size and its output quality. My current weights for my ...
1
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0answers
25 views

different between effect of episodes and time in DQN and where is the updating the experience replay

In DQN paper of DeepMind company, there are two loops one for episodes and one for running time in each step (one for training and one for different time-step of running). Am I right? Since, nothing ...
1
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0answers
18 views

How should Training Data for Fully ConvNets look like?

I've been working with CNNs recently. For a new task, I need to predict objects in an image pixelwise. Fully ConvNets seem to be the way to go. I read the original paper (Long et al., 2014) and a few ...
1
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0answers
61 views

What is difference between training examples generated by continuous bag of words(CBOW) and skip-gram?

This is a simple question that is hard for me: Let's consider simple sentence A B C D and create training examples for skip-gram training (x, y) with number of ...
1
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0answers
30 views

Using binary feedback (good / bad) to train a model with real-valued output

I have a model (which I would initialize with "best guess" parameters) which produces a real-valued output (which I need to be real-valued - I can't switch to a different representation), but for ...
1
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0answers
37 views

Interpreting errors: Is my model unfit for the task or do I just need more data?

I have a convolutional network (For details, please see the edit in the bottom) with training/testing errors that always look very similar to what is shown in the figure. In other terms, it seems that ...
1
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0answers
407 views

Limiting selected variables in Genetic Algorithm Feature Selection

I am trying to find a set of good predictors using carets GA in R to train a few classification models. My dataset consists of around 4500 rows of 96 independent variables. I want to use GA to, ...
1
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0answers
60 views

What does it says about your data and your model if there is not much difference between validation and test data accuracy?

I have modelled (using Adaptive Neuro Fuzzy Inference System) a set of data consisting of 300+ sample points. My data is split into 50% training, 25% test 25% validation. If there is not much ...
1
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2answers
1k views

Interpreting Validation and Training loss

I have a quite big dataset of 10000 training data, i held out 2000 points for validation. I am using a Convolutional Neural Network and using minibatch stochastic gradient descent to minimize the RMS ...
1
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0answers
2k views

What is the minimum sample size required to train a Deep Learning model - CNN?

It is true that the sample size depends on the nature of the problem and the architecture implemented. But, on average, what is the typical sample size utilized for training a deep learning framework? ...
1
vote
1answer
139 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 ...
1
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0answers
807 views

too many ties in knn? how to solve this problem

I use the knn model to train my data and then eliminate accuracy via cross-validation, but when I use the following code, I get the error: ...
1
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0answers
34 views

Should the number of normal samples always be more than that of anomalous samples in training set for anomaly detection?

I am trying to train an anomaly detection algorithm (one-class svm) on a data set with a few hundred positive samples and several thousands negative examples. Is it mandatory that I train the model ...
1
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0answers
40 views

Learning over Multinomial data

I have a training data with 68 features... Each of which is a different multinomial distribution. Eg. Feature 1 can take 1 of 4 values while feature 2 can take one of 10 values. Which classifier or ...
1
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0answers
134 views

Is there any relationship between Train error and Test error in linear regression?

I am doing regularization on linear regression and am observing something arguable: By increasing the $\lambda$ (shrinkage value), I observe that there is a point in which the training and testing ...
1
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0answers
294 views

Can you take a DNN that was trained without regularization, and continue training it with regularization?

If I've trained a DNN with out any regularization methods (e.g. weight decay, dropout etc.) and reached a good training error, can I somehow take that learned net and fine tune it with regularization? ...
1
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0answers
120 views

Adaptive Learning Rate Convolutional RBM?

I was wondering if anyone was aware of some work done for Adaptive learning rate for Convolutional RBM training? KyungHyun Cho published an algorithm for RBM (Enhanced Gradient and Adaptive Learning ...
1
vote
0answers
118 views

semisupervised classification training on all or part of unlabeled data

I have 3 sets of data. A positively labeled dataset. An unlabeled dataset that has for sure positive (around 75%) and negative data. An unlabeled dataset that has for sure positive data and maybe ...
1
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0answers
1k views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
1
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0answers
242 views

Forming training set for Multinomial Naive Bayes

Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance? For example, we forming classifier for three classes - Japan, China, Korea. ...
0
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0answers
14 views

Predicting multiclass outcome using R

The dataset contains about 17000 observations, the outcome variable (with about 10 classes) and the predictors (about 10) are all categorical variables. Which algorithm is the best to employ to train ...
0
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0answers
14 views

Aggregate data for machine learning. Weights or fake disaggregation?

I have a dataset of medical centers and I need to predict their infection rate, based on the center characteristics and aggregated patient data (eg. percentage of patients which underwent a certain ...
0
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0answers
23 views

Gaussian process with ARD kernel much more expensive to train

I'm fitting a Gaussian process regression model in MATLAB (using the quasi-Newton method) with 10 input parameters, using the Matérn 5/2 and Matérn 5/2 ARD kernels. I notice that, with increasing ...
0
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0answers
35 views

Why backpropagation if loss function is not convex in nature?

Backpropagation contains the method of gradient decent, which works well for convex loss functions with a global minima. But, for training, in most of the neural network tasks, backpropagation is ...
0
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0answers
10 views

Cross validation for timeseries interpolation

I am trying to calculate the MSE from interpolating few data points obtained from a sensor. I am considering 100 datapoints and applying 10 fold cross validation to it. For explaining what i am doing ...
0
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0answers
47 views

Specificity decreasing when new features are added to glmnet model for case/control prediction

I'm using glmnet for prediction of case/control, which I created with the function train with additional parameters for cross ...