Questions tagged [train]
training (or estimation) of statistical models or algorithms.
0
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0answers
11 views
Difference between retraining on different portions of data and training initially on larger data set
I have a large data set that doesn't fit in memory and would have to use something like Keras's model.fit_generator if I would like to train the model on all of the ...
1
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1answer
20 views
Determining suitable seed number in R to randomly split data into training and test sets
Good day. I have sample size of 160 and I am randomly splitting them into 70% train and 30% test data sets. My question is about the set.seed() value which returns different random samples in each ...
1
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0answers
10 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 ...
2
votes
1answer
34 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 ...
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0answers
81 views
Very High Training Accuracy and very low Testing Accuracy CNN
I'm using 3 layer CNN with 8, 16, and 32 filters, each of size 5 X 5. I'm getting an training accuracy of 99.97%. Testing accuracy of 41.11%.
Total classes: 605
Train Set: Each class has 7 samples
...
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0answers
12 views
oversampling data with subclass
Oversampling of under-represented data is a way to combat class imbalance. For example, if we have a training data set with 100 data points of class A and 1000 data points of class B, we can over ...
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0answers
18 views
Regularized linear regression with class imbalance
I am trying to build a Linear Regression model using a not so big dataset. I'm more comfortable doing classification and I am not really an expert in regression.
In classification, I was used to ...
0
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1answer
23 views
What to do with very little training data
What are some popular solutions in dealing with very little training data? Do these solutions rely on generating more data (e.g. bootstrapping, SMOTE, etc.)? Or do they rely on methods that do not ...
3
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1answer
46 views
Does retraining a model on all available data necessarily yield a better model?
A (simplified) typical workflow in machine learning might be:
Train $m$ models on a training set.
Validate the $m$ models on a validation set to yield the best model with parameters $\theta$.
Retrain ...
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0answers
16 views
Splitting data into test and training when there are a low number of observations for a level within a variable
Context
If a level within a categorical variable has less observations than there are
elements in the training set then there's a chance that all of the elements of
that level will be contained ...
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0answers
8 views
Different values in test / training data variable
The code and error in the following MWE represent my issue.
code
...
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0answers
27 views
Credit Scoring WoE Calculation
I'm creating credit scoring model and stuck with WoE calculation. I know the formula and I know how to compute WoE for train sample. Should I use train sample WoE for test sample or I should compute ...
0
votes
1answer
35 views
What's this class of algorithms called: (entire training dataset, new input) -> output?
Supervised machine learning algorithms normally work by preprocessing a training dataset and outputting a compact model (e.g. a bunch of regression coefficients) that can quickly give an approximate ...
2
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1answer
40 views
Save Machine Learning Model progress for later [closed]
another dumb question, but how do you save the progress an ML model has made and start from that point later? Its kind of a vague question, but this is an example of what I am talking about:
Say, ...
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0answers
10 views
ANN for Boundary Value Problem
I have a question regarding solving Boundary Value Problems (BVP) using ANNs.
My understanding is that this is currently a challenging task. Most scientific literature on the subject is interested in ...
0
votes
1answer
17 views
With limited training time - should I shrink the training set and run more epochs?
I wonder what is considered a better practice in deep learning. I have a dataset of 100K images that I want to use for training a regional-CNN for a whole week.
Is it possible that my network will be ...
4
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2answers
99 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
127 views
How to train CNN for noise removal from images using Matlab [closed]
I am currently working on a project of mine where I want to use Convolutional neural networks for noise removal from images. I am talking about removing Poisson type of noise. The software that I am ...
2
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1answer
30 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 ...
1
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1answer
27 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 ...
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0answers
76 views
What is the training algorithm for sklearn's Bayesian Ridge Regression?
I read the sklearn's code to train Bayesian Ridge Regression, but can not understand the algorithm. I think it is EM, but don't know where the update equations for lambda_ and alpha_ come from. Any ...
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0answers
17 views
Does maximizing model generativeness maximize its discriminative-ness?
If I train a model M in a way that maximizes the penalized likelihood of M given some data, is this equivalent to maximizing its ...
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0answers
248 views
RFECV + GRIDSEARCHCV on entire dataset (?)
I have 30,000 samples with 150 features for a binary classification problem, now I plan to follow: https://stackoverflow.com/questions/23815938/recursive-feature-elimination-and-grid-search-using-...
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0answers
32 views
If I remove 20% of the train examples, my CV and train score improves, how can I find the reason why?
I have a small dataset - several thousands examples with a lot of features, a lot of them have a lot of zeros. If I remove 20% of examples from the end of the dataset - my CV and train(on changed ...
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0answers
11 views
Why after multiple epoc the performance decrease on the train data?
I have a network and I notice that after multiple epoc the performance on the train data start decreasing. Why?
2
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1answer
100 views
How to meaningfully compute the accuracy of a multi-step forecast produced by a model
I am trying to measure the accuracy of my model in producing a multi-step forecast and I have read a lot of different opinions on the matter and am now rather confused.
The goal of my model is to ...
1
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1answer
201 views
in-sample data vs out-of-sample data
I know that a train-validation-test splits the data into:
a training dataset - obviously my "in-sample" data
a validation dataset
a test data set - obviously my "out-of-sample" data
My question is: ...
3
votes
2answers
375 views
Can overfitting be a good thing in some cases?
I know the goal of machine learning is to create generalizable models and therefore overfitting is undesirable.
However, I wonder if it could be desirable in some cases. For example, let's say I ...
0
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0answers
25 views
How to test a PCA+classifier model? [duplicate]
I have a 100x45 dataset and I'd like to perform feature selection and classification/regression.
I'm currently using various techniques to check which one has the best performances, but I have a ...
0
votes
1answer
122 views
Why is gradient descent and it's variants used instead of BFGS and L-BFGS for training neural nets? [duplicate]
My understanding is that BFGS and L-BFGS solve the same type of optimization problems as GD and it's variants.
Why is GD the go to algorithm for training neural networks?
1
vote
2answers
90 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
vote
0answers
267 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 ...
0
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0answers
36 views
What a convex Precision-Recall curve means for training dataset?
Situation
I have trained a GBDT model(gradient-boosted decision tree, a tree ensemble model) with a training dataset, and when I calculate PR curve on the same training set, it looks convex:
For ...
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0answers
218 views
GAN training: Both G and D has very low loss
I am training a GAN. At the beginning the generator has a very high loss, which converges over time. After some time, the image quality seems pretty good, but both the generator and discriminator have ...
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0answers
32 views
How to train a NN with multiple outputs when not all of them are known in a test set?
I am working on a boundary value problem. So far I am using the implementation from the following paper Aarts, L.P. & van der Veer, P. Neural Processing Letters (2001) 14: 261. https://doi.org/10....
0
votes
1answer
15 views
Should the distribution of my samples in my model be equal to to the original data set?
In my original data set I see a distribution of 70% belongs to label A and 30% belongs to label B. For my train, validation and test set I maintained the same ratio. However, I wonder whether this is ...
0
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0answers
102 views
How to implement the Walk-Forward optimization for RNN
Say you have 24 time steps in total, and you are trying to fit an RNN model. You designate the most recent 5 terms as test period, and the others as train period.
In the training process, you start ...
2
votes
1answer
51 views
Standardization on training only or also including testing data?
My question is very much related to this one:
How to apply standardization/normalization to train- and testset if prediction is the goal?
However, my testing data is not a single observation that I ...
0
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0answers
182 views
How to Split Time Series Data to train/test for RNN [duplicate]
Let's say I have a set of time series data with 32 time steps.
My goal is to predict what the data value would be for the next time step, given data for 30 previous time steps. Would it be okay to ...
1
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0answers
132 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 ...
0
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0answers
35 views
Accuracy varying considerably depending on selection of test/train set
I have a large database that is being used for a classification problem. The original total database is being partitioned 80% into sample 1 (where 80% is for training and 20% for validation) and 20% ...
1
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0answers
21 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
vote
1answer
165 views
Bootstrapping accuracy, f1-score?
I have typical train/test setting, with an ordinary dataset. As I am comparing performance of two approaches to a problem (namely churn prediction with AdaBoost and ...
1
vote
1answer
120 views
In which scenarios are the in-sample error and training error NOT the same?
In Elements of Statistical Learning, Chapter 7 (pages 228-229), the authors define the optimism of the training error rate as:
$$
op\equiv Err_{in}-\overline{err}
$$
With the training error $\...
4
votes
0answers
408 views
When to stop training of neural network when validation loss is still decreasing but gap with training loss is increasing?
During training of CNNs, I often come across this case for training and validation loss :
X axis is epochs, Y axis is cross entropy loss.
I would like to keep the "best model", meaning the one which ...
3
votes
1answer
44 views
Can a ConvNet see patterns that a human cannot?
I am training a ConvNet to detect different types of stripes in my images. As I am working on astronomical images, my pixel values are flux densities and therefore represent ground truth data.
When I ...
1
vote
1answer
168 views
Interpretation of logistic regression with normalized features
With logistic regression, a one unit change in $X_1$ is associated with a $\beta_1$ change in the log odds of 'success' (alternatively, an $\exp(\beta_1)$-fold change in the odds), all else being ...
3
votes
2answers
2k views
Is there a way to incorporate new data into an already trained neural network without retraining on all my data in Keras?
I have already trained a neural network on my data. In the future, I will receive some more data. How can I incorporate this data into my model without rebuilding it from scratch?
4
votes
3answers
134 views
When is there a difference between a normal likelihood loss and a least squares loss?
My understanding is that if the errors follow a normal distribution, then using a maximum likelihood loss or a least squares loss to train a model amounts to the same thing.
However, I am looking at ...
1
vote
1answer
230 views
train / validation / test split
I understand that you typically use three different data sets (train/validation/test) to acquire an unbiased estimate of the performance measurement, because the models are tuned to fit for the train ...
