Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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3answers
557 views

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

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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0answers
25 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
33 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 ...
0
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1answer
27 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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0answers
9 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 ...
2
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1answer
22 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
39 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
14 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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0answers
10 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 ...
3
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1answer
72 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
52 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
11 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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0answers
33 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
27 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, ...
0
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0answers
22 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
37 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
26 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
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, ...
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0answers
20 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
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0answers
18 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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0answers
22 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
49 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
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1answer
26 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
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0answers
38 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
17 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
15 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
31 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 ...
15
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5answers
3k 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
71 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
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0answers
45 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
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0answers
58 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
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0answers
102 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
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1answer
74 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 ...
2
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1answer
441 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
31 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
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1answer
18 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....
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0answers
50 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 ...
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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
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0answers
55 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
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1answer
60 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
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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
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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
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1answer
159 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
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0answers
26 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
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1answer
213 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
315 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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