Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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49 views

Why not use large ($n - [n^{0.75}]$) validation sets in machine learning training loop?

I am seeking feedback in the form of answers to a bold question. Please limit comments to requests for clarification of the question only. There appears to be an influence on machine learning ...
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0answers
12 views

Uplift modeling for Train, validation, test data sets

I am wondering when I should tune hyper parameter when we build uplift model. In a normal machine learning context, data will be split into train, validation, test. And, we train the model with train ...
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10 views

training variational autoencoder - loss is 0 val loss is 0

I am training a VAE in Keras on the colab. The issue that I am encountering is that after the first epoch the loss is 0 and the validation loss is 0 (but the model didn't learn anything). this is the ...
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34 views

Why does switching the architecture result in nans during training?

What are my options to figure out the casue of spontaneous nans/infs during training? Basically I changed the model from a custom resnet to the vanilla resnet(and also later a vggnet varation) and ...
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2answers
40 views

R2 on out-sample data set

The conventional definition of $R^2$ is: $R^2 = 1-SSE/SST$, where SSE denotes sum of squared errors and SST is total sum of squares ($n\times variance$, n being number of sample points in train set). ...
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19 views

Small sized training set and results varying based on cross-validation split

I need to try to build a classifier based on around 30 instances. The outcome can also be that the dataset is not large enough for this purpose, however I'm not sure on how can I justify this outcome ...
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1answer
44 views

Does it make sense to regularize the loss function for binary/multi-class classification?

When discussing linear regression it is well known that you can add regularization terms, such as, $$\lambda \|w\|^2 \quad \text{(Tikhonov regularization)}$$ to the empirical error/loss function. ...
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14 views

Using a poset or directed graph as input for a neural network

I'm not sure if this is the right community to post this in but I would appreciate any help. As the title states, I'm trying to train a neural network using some unconventional input. I'm wondering if ...
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0answers
7 views

How to train sentiment analysis with neutral

I want to classify sentiment on blog and forum sites. Each post can be positive, negative or neutral. Most of the existing sentiment classification are for two classes, positive and negative. I am ...
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0answers
20 views

Unit Deviance on test set R

I am evaluating a set of predictions coming from different models against a set of actual values in a regression problem. I do not want to use the mean squared error as my evaluation metric because my ...
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1answer
15 views

validation error and test error when limited data is available

In machine learning, to get an unbiased estimate of model performance, we split data 80:20 into train and test set. We use the training set for model training and model selection according to cross-...
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0answers
8 views

How easily train model with set of hyperparameters with different algorithms?

I have tried hyperprameter optimization and do get top 15 best combination of parameters using gs.cv_result but now I would like to train a model on these 15 combination of hyperprameters seperately....
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36 views

Neural network training converges for several epochs, then diverges badly

I have a VGG-like network that I have trained from scratch on a multi-class dataset of my own. The results suggested there were probably some data errors somewhere, so I thought I would train the same ...
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0answers
37 views

Predicting when and where a conflict is most likely to take place (glm/ train test set error)

I am having difficulty analysing this dataset (I am new to statistics and R). I am trying to model when a conflict is most likely to take place (before, during or after deforestation), and at what ...
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3answers
609 views

Is it in general helpful to add “external” datasets to the training dataset? [closed]

Several people have already asked "is more data helpful?": What impact does increasing the training data have on the overall system accuracy? Can increasing the amount of training data make ...
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28 views

How to evaluate xgb.cv and is it okay to use min(test_logloss) even if there is large gab between train_logloss?

Please migrate if this is not correct spot the post this question. I have a dataset including 70K - 1s and 300K - 0s, a type of classifier. I am using Xgboost to ...
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0answers
13 views

To what extend can pre-training and training affect the results of a prediction model?

If we pre-train a model forecasting COVID-19 with data of SARS, which had a different transmission pattern, will our model be weakened? If we train the same model with data of (for instance) the USA, ...
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1answer
41 views

Training of a deep Artificial Neural Network

I have few doubts related to training a neural network with more parameters (weights and biases) than number of data points. I know there exists discussion (on this platform) related to training such ...
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1answer
30 views

Why does shuffling in train test split have a big impact with my loss and accuracy?

I used Keras for the train test split. This is what I get when I shuffle during my train test split: When I disable shuffle by setting shuffle:False this is what I ...
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13 views

svm train score is 1.0 and test score 0.996. How to verify its correct?

My dataset has features CO2, PM2.5, Temperature and Humidity and I am trying to predict the Air Quality Index based on this information. I have approximately 24,000 data points for each of the ...
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1answer
25 views

Is it possible to overfitting within single epoch

Let me put my question first. For a time-series prediciton, is it possible to overfit even within the first epoch, when training data and validation data should all "new" to model? Features and ...
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0answers
41 views

Machine Learning: Why do I have this pattern of train and validation accuracy?

I am trying to understand what would generate this pattern of accuracy in train and validation dataset (second and third plot below). I am training a network to recognize 6 types of faces (they are ...
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0answers
55 views

Can the size of the training set of a neural network be smaller compared to other models?

If some of you knows what is the rule thumb one in ten rule this what I want to discuss. Neural network can, nowadays, go deeper in performances. Did you find, according to your experiences, that you ...
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2answers
16 views

Issues with training on a sample of training set?

I am training an SVM on highly imbalanced data. I have rectified this issue and my ML pipeline works just fine. I have allocated 70% of my dataset for training, however this takes an infeasible amount ...
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13 views

How to improve neural network training against a large data set of points with varying magnitude

I am currently using TensorFlow and have simply been trying to train a neural network directly against a large continuous data set, e.g. $y = [0.014, 1.545, 10.232, 0.948, ...]$. The loss function in ...
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1answer
108 views

What is the relationship between mean squared error and classification error?

I've trained a network using a genetic algorithm and I have two possible fitness functions for my GA: MSE and CErr. If I use MSE as my fitness function, over time MSE decreases and classification ...
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1answer
259 views

Keras: What is the meaning of batch_size for validation?

If I understand correctly that batch size is the number of samples used in the training of a NN before the gradient gets updated, then why do we need a specified batch_size for the validation sample? ...
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0answers
12 views

Can I stop training my neural network at this point?

When the validation error of my Neural Network that I am trying to train is slowly decreasing but not by much, is it okay to stop train the network at that point, or do I need to increase the training ...
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36 views

How to estimate the performance of Neural network

I have a feedforward neural network with two hidden layers built in keras. let say I have 40 observations. I split the data into train (e.g., 35 observations) and test (e.g., 5 observations) sets. ...
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2answers
29 views

What are the best models/methods for training when the target is multidimensional?

I am not very familiar with all methods in Machine Learning. However, I know for example when I apply linear regression, the y is always assumed to be one-dimensional. My target is multi-dimensional. ...
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0answers
8 views

Train a neural network as part of other ML algorithm

I want to implement some kind of neural network as "encoder" that encode my input, transform it into other dimension and use the output (encoded data) in other algorithm like kNN etc. My question is, ...
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0answers
25 views

Increased computation time for training and prediction with reduced feature space?

I implemented a PCA algorithm to reduce the input feature space of my neural network from 230 to 110 features. My naive expectation was that if I train a neural network using the same hyper ...
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0answers
41 views

Data leakage: Does it make sense to split time series this way?

I’m trying to make sense of how to create the training data for time series. Let’s say we have sales data: ...
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1answer
29 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,...
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0answers
42 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, ...
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0answers
22 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 ...
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0answers
21 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. ...
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23 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: ...
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0answers
68 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 ...
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1answer
27 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 ...
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0answers
50 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 ? ...
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0answers
19 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
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1answer
24 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 ...
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0answers
18 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
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1answer
36 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 ...
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5answers
4k 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
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1answer
89 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, ...
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1answer
120 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 ...
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1answer
745 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
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1answer
43 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 ...

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