Questions tagged [train]

Training (or estimation) of statistical models or machine learning algorithms.

Filter by
Sorted by
Tagged with
0 votes
2 answers
29 views

Comparing impact of training data size - what testing data size?

I am training a classifier using BERT and want to check how the accuracy changes with increasing training data size. Up until now, I have 1k annotated training samples and tested the accuracy for ...
user avatar
  • 3
5 votes
2 answers
75 views

Are there any way of removing impact of a certain data from a trained model (about "right to forget")

I was reading about wearable technologies (Recent Advances in Wearable Sensing Technologies). They briefly talk about Right to forget and a question came to my mind. Suppose that we trained a deep ...
user avatar
  • 189
0 votes
0 answers
58 views

Does it make sense that the loss function for traning and evaluaton is different?

Huber loss function is widely used, because it combines the good properties of squared and absolute losses. Therefore, when I apply the penalized regressions, i.e. LASSO, Elastic net and Ridge, to ...
user avatar
1 vote
0 answers
18 views

Variable Length Input: How should variable-length input data be handled during the testing stage?

I have data that is sequential. Here, I am showing a toy example of my data in the following image: I need to input the data into the model as groups of samples based on the class duration. To ...
user avatar
  • 121
0 votes
0 answers
10 views

Getting rid of the additive degree of freedom for discriminators of WGAN-GP's

Setting: Discriminators in WGAN-GP's are trained to minimise the following loss functional over functions D: Here I have been playing around with training a critic (simple convolutional network ...
user avatar
0 votes
0 answers
32 views

How to compare cross-validation results against test results (XGBoost model)?

I am building a gradient boosting regression model with XGBOOST and testing different versions of the model by adding or modifying some features. The target variable is a skewed continuous variable. I ...
user avatar
  • 1
1 vote
1 answer
16 views

How to scale data for model retraining on production?

Let's say I have a basic regression model being used in production and now I want to implement periodical model retraining (i.e. once a month) where I take a batch of new data from last month and fit ...
user avatar
  • 121
1 vote
0 answers
58 views

Is it correct to train and validate the model on F1-score metrics?

I am trying to do experiments on multiple data sets. Some are more imbalanced than others. Now, in order to assure fair reporting, we compute F1-Score on test data. In most machine learning models, we ...
user avatar
  • 121
3 votes
1 answer
99 views

What is meant by siamese network: train one network for each class or one network for all classes (example of training face recognition)

In siamese networks, the aim is to make closer the data from the same class and push far away the data coming from the different classes. Suppose that we want a face identification system with 5 ...
user avatar
  • 189
2 votes
1 answer
64 views

How to split dataset into training and testing when intending to go into production?

I am doing a customer retention/churn prediction project where I have a dataset where each row comprises a customer's data/activity. Each column comprises the past 6 months of a customer's activity/...
user avatar
2 votes
1 answer
19 views

What's the official name of the "crop test"?

I call "crop test" or whether my model passed the "crop test" when I remove data from my dataset, conveniently before some events in the data to check whether the historical ...
user avatar
  • 875
0 votes
0 answers
24 views

Negative KL divergence for train_test_split in sklearn for y_train and y_val

So, I am trying to understand if I have fair split of my train and val sets using train_test_split of sklearn, so I decided to run the KL divergence and JS div tests and I get the following results. ...
user avatar
0 votes
1 answer
66 views

Train Test Validation standard split vs Cross Validation

This is a simple doubt… I am confused with the conceptual difference between a Train | Validation | Test split and K-fold validation. In K-fold, I understood, We train and validate on everything ...
user avatar
  • 1
0 votes
0 answers
16 views

How Should Learning Rate, Warm Up Learning Rate, Weight Decay, and Other Training Parameters Be Scaled With Batch Size?

Currently, due to memory limitations, I am scaling my batch size by a factor of k, which probably means I need to scale other factors of my algorithm too. For learning rate, I have heard that I should ...
user avatar
0 votes
0 answers
39 views

Query regarding Deep learning model performance reporting

I am working on Human activity recognition via smart device sensors data by using deep learning. However, I am confused to report the results of my deep learning architecture. Therefore, I would like ...
user avatar
10 votes
4 answers
2k views

I've already used my entire dataset in a regression, should I not use that as a prediction model?

At the hospital I work at we were writing a paper on what variables about a patient predict whether they'll return for a follow-up visit. We included variables such as age, gender, distance from ...
user avatar
0 votes
0 answers
27 views

Validation loss stop decreasing in early stage of training

I'm training a LSTM+CRF model for sequence tagging. However, the validation loss of the model stops decreasing at early stage of the training. The below is the train losses and validation losses. The ...
user avatar
0 votes
0 answers
36 views

How can the scores of CV done with training data better then the score of all training data

I have the following issue, when I use the adam solver (MLPRegressor from sklaern) my cross validation (10 repeats a 5 splits) metrics (r2, maxError, RMSE, MAE) are all better (r2 ~ 0.94) as the ...
user avatar
0 votes
0 answers
20 views

Training a multi label classification where each example should only create an error for certain labels during training

Imagine the following problem: You want to predict how likely it is that a person (with a set of features that you can train on) who visits a certain country will also go and see the capital city. So ...
user avatar
0 votes
0 answers
20 views

Continuation of Training for Neural Networks

originally posted in SO Artificial Intelligence, advised to post here instead Background I am developing a GAN model (based off this paper), and am trying to learn how to feed subsequent time series ...
user avatar
  • 608
0 votes
0 answers
32 views

Stratified sampling - use of proxy variable

For splitting of the data into train/test/val I use stratified sampling. Is it appropriate to define strata using information extracted from the dataset? E.g. use machine-learning to model proxy ...
user avatar
2 votes
1 answer
44 views

Time Series Forecasting Process With Regard to Training and Test Sets

I'm a bit confused about the process order in doing proper time series analysis/forecasting. Is it: Stationary/seasonality checks, do any transformations required Candidate model selection using ACF, ...
user avatar
0 votes
0 answers
15 views

Recurrent neural networks with loss of data

I want to train a recurrent neural network (RNN) for making predictions of some data. I have 8 variable inputs and with them I have to make predictions of other two variables (outputs). I need RNN to ...
user avatar
  • 1
0 votes
0 answers
113 views

How does glmnet in caret choose the values of lambda and how does it compute coefficients of the model?

I have a question that I've been struggling with. My students are asking me, but I can't figure it out myself. When I train LASSO regression in R caret, I use the method "glmnet" and a grid ...
user avatar
1 vote
0 answers
11 views

Training in steps has any importance?

I'm trying to train a Siamese network for face Verification and eventually I came across the Contrastive Loss method for embedding vector distancing (kinda... I ...
user avatar
0 votes
0 answers
16 views

How do you use a weight matrix used for a batch in a final model to only take a single input?

Setup: An input vector x to a neural network has 5 components, xi, i=1,5. Say a weight vector is multiplied by x, w = [wi], i=1,5: so dot(w,x) = sum_i (wi * xi) Now when doing batch training, say 10 ...
user avatar
  • 1
0 votes
0 answers
26 views

Validation Loss gets better after adding Augmentation Layers but Test accuracy gets worse

I'm building a Siamese Network which should learn a face comparison function. My model consists a CNN (which gets 2 inputs, and yields 2 embedding vectors). With the outputs I calculate: ...
user avatar
0 votes
0 answers
12 views

The implication for using only data points with extremely positive and negative labels for classification

Here I use the Quora dataset from Kaggle to explain my question. The label column of this dataset is is_duplicate and has value of 0 or 1. 255,043 question pairs in the dataset are labeled as 0(not ...
user avatar
  • 643
1 vote
0 answers
47 views

How to improve model generalization with small dataset?

I have a fairly small dataset (45 data points (i.e populations) where I took plant information. I'm running a random forest regression on my measurements and climate information to predict a feature, ...
user avatar
  • 41
0 votes
1 answer
398 views

About Epochs and how many of it?

I'm pritty new to the machine learning world, and I ws trying to figure out how many epochs should I run my training CNN model on the MNIST dataset (which has 60,000 training images and 10,000 ...
user avatar
0 votes
1 answer
168 views

Loss values above 1.0

I have a convolutional neural network for tensors classification in Pytorch. I am using Cross-Entropy Loss. My optimizer is Stochastic Gradient Descent and the learning rate is 0.0001. The accuracy of ...
user avatar
  • 31
0 votes
1 answer
318 views

Train/Test split in multiple time series data

Currently, I have log files from 10 machines. Each log file contains an event type and the occurrence time of the event. Each machine's log file is recorded under the same time frame, and I wish to ...
user avatar
1 vote
2 answers
971 views

Why validation accuracy is increasing very slowly?

My convolutional network seems to work well in learning the features. However, the accuracy of the validation set is increasing very slowly with respect to the learning rate as also illustrated in the ...
user avatar
  • 31
0 votes
0 answers
23 views

When training data is much less than the prediction perdio

Given training data on tweets and their retweets, how would you predict the number of retweets of a given tweet after 7 days after only observing 2 days worth of data? Its strikes me that this ...
user avatar
  • 379
2 votes
1 answer
97 views

Does training time of CNN includes the validation time performed after every epoch?

I want to record train time of a CNN architecture in keras. My question is that while we are training a CNN, we also validate the model after every epoch to monitor that how well it generalizes which ...
user avatar
0 votes
0 answers
26 views

Correct way to use cross-validation for hyperparameter selection when testing a model in multiple trials which use different train-test split

I'm going to evaluate a model with some benchmark datasets. I want to perform 100 trials of training and testing of the model for each dataset, and I want to randomly split the data again in training ...
user avatar
2 votes
1 answer
3k views

How to choose a batch size and the number of epochs while training a NN

After searching I read diferent theorys that using a greater batch size has better performance while model is training, but in the other hand, I also find the oposite view, that using a mini-batch ...
user avatar
3 votes
2 answers
265 views

MultiClass Classification - Training OvO and OvA

I like to know how OvO (One vs One) and OvA (One vs All) models are trained in multiclass classification problem. To keep it simple, we have 4 classes, each of which has 1000 datapoints. What are the ...
user avatar
0 votes
2 answers
93 views

(How) can model parameters be learnt using MCMC?

I get stuck by part (b) of figure 4 in this paper: Hands-on Bayesian Neural Networks - a Tutorial for Deep Learning Users. In my understanding, inference algorithms like MCMC are not for training ...
user avatar
  • 5,151
0 votes
1 answer
41 views

When may the Kernel Trick Matrix be non symmetric?

Ridge Regression can be expressed as $$\hat{y} = (\mathbf{X'X} + a\mathbf{I}_d)^{-1}\mathbf{X}x$$ where $\hat{y}$ is the predicted label, $\mathbf{I}_d$ the $d \times d$ identify matrix, $\mathbf{x}$ ...
user avatar
  • 224
1 vote
0 answers
40 views

Random forest outside range of training - Alternatives?

Statistical models in general are not well suited for applications outside the training range. Using a random forest binary classifier aimed at detecting an event, I've trained it using 100 data ...
user avatar
  • 127
15 votes
2 answers
2k views

Can I (justifiably) train a second model only on the observations that a previous model predicted poorly?

Say I commit the following sins while building a predictive model: I take my dataset and split it into four subsets: Three for training (Train_A, Train_B, and Train_C) and one for validation. I ...
user avatar
  • 155
1 vote
0 answers
33 views

Training Individual-Level Predictor using Distribution of Group-level Data

I have a problem in which I'm looking to train an individual-level predictor for outcomes. I have information on individual-level covariates, but I don't have individual-level outcome variables. ...
user avatar
0 votes
2 answers
115 views

What are best practices around combining train/val/test splits when training a production model?

Say I'm training a deep neural network and I have split my data into train, val, and test splits. I have trained many models on the train data and then using the val data during the training loop for ...
user avatar
  • 358
3 votes
2 answers
434 views

Split data into test, training and validation when some patients have multiple observations

I have a dataset that has multiple observations for some patients. Patients are labelled by patient IDs and I would like to split the data into testing, training and validating groups as I am trying ...
user avatar
  • 31
0 votes
0 answers
90 views

How to force a neural network to uniformly decrease MAPE?

I aim for replicating an numerical (non stochastic) algorithm by a neural network. Therefore I have basically an unlimited amount of data and I wish that the network have an almost perfect fit in ...
user avatar
  • 11
1 vote
0 answers
40 views

Can I set batch size to 1

I am trying to train a T5 (t5_large) transformer model on some data. Since it's out of cuda memory, I was forced to set ...
user avatar
  • 479
1 vote
1 answer
239 views

Is it possible to train two neural networks combined with same output?

Say I have two kinds of input that I want my neural network to learn a possible from: one 2D image some 1D metadata about the image This case and similar cases seem problematic to me because the two ...
user avatar
  • 123
1 vote
0 answers
21 views

If I know specific pair of characters that model confuses in OCR task how can I fixe it?

I train OCR model to recognize cyrillic handwritten text. I know, for example, that it confuses very often 'Б' with '6'. How can I use this information to fine tune the model ? Just in case, my ...
user avatar
1 vote
0 answers
85 views

Machine Learning Classification of Changes in a Time Series

I would like to classify the changes in the time series depicted in the figure below—representing an angular position history of a robot about the vertical axis—into increment/decrement of roughly (...
user avatar
  • 11

1
2 3 4 5
7