Questions tagged [train]
training (or estimation) of statistical models or algorithms.
251
questions
12
votes
5answers
2k views
Can increasing the amount of training data make overfitting worse?
Suppose I train a neural network on dataset A and evaluate on dataset B (that has a different feature distribution than dataset A). If I increase the amount of data in dataset A by a factor of 10, is ...
3
votes
1answer
32 views
About the need of splitting data in stacking
I learned stacking of machine learning in a book, hands-on machine learning 2nd edition (2019).
The picture was cited from hands-on machine learning 2nd edition (2019).
In the above situation, ...
0
votes
0answers
15 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
votes
0answers
24 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
votes
0answers
44 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 ...
1
vote
1answer
21 views
Test score bigger than Train score in Linear Regression
I'm new to ML and I'm trying to create a linear regression model. My data consist of 100 samples with 4 features each. This is my humble code
...
1
vote
1answer
98 views
Why my validation accuracy and AUC are higher than my training accuracy and AUC?
I have a binary classification problem and I use LightGBM classifier to build my model based on 5 features. I divided my dataset (94 observations) into two parts:
Training dataset: 60 observations
...
1
vote
1answer
16 views
Can we apply SMOTE on data with k-fold CV
The SMOTE for the imbalance should be applied for the training data only, right? Can we still do it (perform SMOTE on training data) while we select the k-fold CV and does not go for splitting the ...
0
votes
1answer
15 views
Removing parameters from the training set helped training
I have a large dataset with 10s of millions of points in a 10 dimensional parameter space. I have tried training my regression neural network on the entire parameter space and got decent (ish) results....
0
votes
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
votes
0answers
48 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 ...
0
votes
0answers
18 views
Training a deep neural network using a changing (increasing) dataset
I am trying to train a deep autoencoder (applies also for other architectures) in the following way:
Step 1) I start with a fix dataset of e.g. 10k samples.
Step 2) A training "loop" consists of ...
1
vote
0answers
25 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
vote
1answer
57 views
Logistic regression - what is being predicted? [closed]
R has more than one way to create logistic regressions to predict binary outcomes.
Here's the code that I'm using that is giving strange answers.
...
0
votes
1answer
20 views
Randomize dataset for Restricted Boltmann Machines
Suppose I want to train a RBM (or even a DBN architecture) and then fine-tune the parameter training a Feedforward NN. In my case the dataset is composed of time series, so in principle there is a ...
0
votes
0answers
21 views
synthesized data to train classifier
Our dataset is relatively small (303 x 14) and so we decided to use synthpop package in R.
The basic idea of synthetic data is to replace some or all of the observed values by sampling from ...
0
votes
0answers
13 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
vote
1answer
37 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
vote
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 ...
3
votes
1answer
78 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 ...
0
votes
0answers
236 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
...
0
votes
0answers
14 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 ...
0
votes
0answers
25 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
votes
1answer
31 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
votes
1answer
127 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 ...
0
votes
0answers
18 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 ...
0
votes
0answers
14 views
Different values in test / training data variable
The code and error in the following MWE represent my issue.
code
...
0
votes
0answers
67 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
43 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
votes
1answer
46 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, ...
0
votes
0answers
19 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
votes
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
votes
0answers
307 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
votes
1answer
99 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
vote
1answer
34 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 ...
0
votes
0answers
139 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 ...
0
votes
0answers
30 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 ...
0
votes
0answers
448 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-...
0
votes
0answers
37 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 ...
2
votes
1answer
135 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 ...
2
votes
1answer
421 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
444 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
votes
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
212 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?
2
votes
3answers
143 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
528 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
votes
1answer
16 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 ...
2
votes
1answer
74 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
votes
0answers
289 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 ...
