Questions tagged [train]
training (or estimation) of statistical models or algorithms.
260
questions
1
vote
1answer
18 views
Cross validation in trainControl function
If we use cross validation in trainControl function, still do we need to perform the prediction on test set or training data in train function is sufficient?
I split the data in training and testing,...
1
vote
0answers
41 views
is training dataset of machine learning are Big Data processing results? [closed]
I need to understand the application of machine learning in big data processing. I am so confused with the concept of big data processing and machine learning and I need clear responses. In general, ...
0
votes
0answers
19 views
Model loss stays the same for hours before dropping
I'm training a CNN to colorize images. The model I have is not incredibly deep, and should work fine on the card I'm training on (2080 TI). Initially, I suspected the model was flawed in some way ...
2
votes
0answers
13 views
Goodness of Fit Test vs Testing RMSE
I have a bit of a broad question. It seems to be that there are two different approaches (Borrowing a bit of Breiman's Two Culture's paper) when it comes to testing if our data looks like our model.
...
0
votes
0answers
21 views
Modeling when unlimited data generator is available
For simplicity, a regression task is needed to be done to model the inverse function of f(x). Let us say:
...
0
votes
0answers
23 views
Oversampling/Undersampling in respect to Train and Test - Isolation Forest
I've got a quite imbalanced data set.
144.496 : 162 -> ratio of 1000:1
I would like to use IsolationForest to detect the 162 anomalys.
I've already split the data.
However, the iForest doesn't ...
0
votes
0answers
19 views
Validation loss is decreasing, accuracy is decreasing too
So, I have the following charts from my experience.Can any one explain why accuracy is decreasing while the loss in train and validation is decreasing? The point is that i can't early stop too in the ...
0
votes
0answers
14 views
object detection loss
I have trained an ssd detector in my own dataset and the values of train loss and val loss are shown in the picture. However in all the epochs the value of val loss is lower than that of train loss ? ...
0
votes
0answers
14 views
I have capped my response variable, should I calculate my RMSE/MAE/MAPE with the true values capped or not?
So, I have trained a model in my train set with the response variable with a superior limit. Because, the peaks are not important for my analysis. And if I dropped it, I would lost a lot of data.
...
2
votes
1answer
23 views
Should a model be trained until it is stable to find optimal hyperparameters?
A model may take several days to train until it reaches an equilibrium - say if the change in error between epochs is lower than some threshold $\epsilon$, or accuracy reaches some equilibrium.
When ...
0
votes
0answers
10 views
Is it possible to achieve both stratified sampling and keeping the same train/test dataset split across different runs?
Generally, it is suggested to sample a dataset such that test set and train set remain the same when running the code multiple times, for comparison but also to hide your algorithm the whole dataset. ...
1
vote
1answer
20 views
should I re-initialize my optimizer and my scheduler before I try to fine tune my neural network on the different dataset?
I am doing NLP, and I have this block of Transformer body that was already trained on dataset A.
Now I am interested in fine tuning this same Transformer on a new dataset B.
In my Python code, should ...
14
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
42 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
18 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
35 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
55 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
34 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
159 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
22 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
16 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
49 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
32 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
23 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
45 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 ...
2
votes
0answers
22 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
97 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
260 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
26 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
34 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
161 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
15 views
Different values in test / training data variable
The code and error in the following MWE represent my issue.
code
...
0
votes
0answers
72 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
53 views
Save Machine Learning Model progress for later [closed]
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, hypothetically speaking, if I ...
0
votes
0answers
20 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
111 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
352 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
156 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
36 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
38 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
156 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 ...
