Questions tagged [training]

Training is the part of machine learning whereby a model is "trained" on a define portion of a dataset to learn attributes and statistical features of the data. It's counterparts are called Testing and Validation. After training a model is tested and validated on another portion of the dataset.

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Training loss decreasing while Validation loss is not decreasing

I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early stopping, various learning rates,...
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How are parameters selected in cross-validation?

Suppose I'm training a linear regression model using k-fold cross-validation. I'm training K times each time with a different training and test data set. So each time I train, I get different ...
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NNs for fitting highly oscillatory functions

in a scientific computing application of neural networks, I have to maximize several neural networks with scalar output with respect to a target/loss function (coming from a weak form of a PDE). It is ...
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What will happen if we train a model on a dataset sorted by class

Suppose we have a dataset of two classes (0 and 1) divided into over 12k mini-batches where the first half of the dataset (over 6k mini-batches) belong to class 0, and the other half belongs to class ...
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Binary classification from local and global feature selection

I want to train a deep leaning model, consisting of images. My question is which scenariowas chosen to train the model? scenario 1 : I train images local context on Output 1, and I train images clobal ...
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Coefficients values in filter in Convolutional Neural Networks

I'm starting to learn how convolutional neural networks work, and I have a question regarding the filters. Are these chosen manually or are they generated by the network in training? If it's the ...
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Is it possible for the (Cross Entropy) test loss to increase for a few epochs while the test accuracy also increases?

I came across the question stated in the title: When training a model with the cross-entropy loss function, is it possible for the test loss to increase for a few epochs while the test accuracy also ...
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Creating synthetic data to increase the training data for ML model

I was wondering if I can create more data from the available dataset to train my model and what are the pitfalls of such a practice? I have a modest number of examples in my dataset, but I believe ...
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How to create the training set?

I am working on a project related to Machine Learning in wireless communication. Here I have to prepare the training set as that consists of training features and training labels as: $S = [(x_1,y_1),.....
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Standardization in combination with scaling

Would it be ok to standardize all the features that exhibit normal distribution (with StandardScaler) and then re-scale all the features in the range 0-1 (with <...
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Does eval loss decreasing slower than train loss indicate overfitting?

I am training a binary classifier using an efficientnetv2 model with a 1M image dataset where I do a 60/20/20 split. Does this graph mean that the model is over-fitting? I can see that the train loss ...
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Splitting Subject Data in train, validation and test set for 3D Human Pose Estimation for better accuracy

This is a 3D Human Pose Estimation problem. There are totally 15 normal subjects in train set, 7 normal subjects in validation set and 7 normal subjects in test set. There are 7 impaired subjects ...
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Splitting Subject Data in train, validation and test set for 3D Human Pose Estimation

This is a 3D Human Pose Estimation problem. There are totally 15 normal subjects in train set, 7 normal subjects in validation set and 7 normal subjects in test set. There are 7 impaired subjects ...
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Training data for anomaly detection using LSTM Autoencoder

I am building an time-series anomaly detection engine using LSTM autoencoder. I read this article where the author suggests to train the model on clean data only in response to a comment. However, in ...
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Cross Validation after using train-test to decide optimal algorithm to use?

I am interested in training different algorithms on a data set and observing performance metrics. Currently, my approach is to train different algorithms on train data, and then evaluate performance ...
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How to use teacher forcing in a LSTM

For my timeseries problem it seems obvious to use teacher forcing. For example in the case of predicting the new timestep in a real life scenario, I do have access to all the ground truths for all ...
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How to train-test split a timeseries?

I have a dataset consisting of multiple timeseries for multiple users. So per user I have multiple timesteps, a value to predict per timestep and a list of features per timestep. I am currently ...
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ValueError: Found input variables with inconsistent numbers of samples: [1599, 1600]

I have the following code sample shown in the image: Both x and y have the same rows but the splitting fails. I am pretty new to this.
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What causes explosion in MSE when training?

I (probably) well overfitted/overtrained a model. But I was just curious as to what might cause this type of behaviour. I carried on training (Epoch 1/50 is not the first epoch of training this model)....
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Comparison of zero-shot learning, one-shot learning, and few-shot learning?

What are the differences between zero-shot , one-shot , few-shot learning? and what about their difference in usage/ application? fields of their application? Comparisons of their Pros & Cons?
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How is model evaluation and re-training done after deployment without ground truth labels?

Suppose I deployed a model by manual labeling the ground truth labels with my training data, as the use case is such that there's no way to get the ground truth labels without humans. Once the model ...
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Reinforcement Learning vs Retraining

I have created a complex ML model using supervised learning. For the sake of discussion, let's say my model identifies dogs and a human labels the output as "correct" or "not correct&...
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Can I train the model using previously saved model?

If I have trained an LSTM model and saved it and after few months if I have new data can I use the saved model to train it on new data? Why I am asking this is because say if I use the saved model to ...
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Training Loss or Validation Loss for Hyperparameter Optimisation

When performing HO, should I be looking to train each model (each with different hyperparameter values, e.g. with RandomSearch picking those values) on the training data, and then the best one is ...
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TypeError: bad argument type for built-in operation

I have one dataset containing images X of type ( numpy array) and one target csv file as Y which has counts of cells (type : pandas dataframe, that I have converted to numpy array), both are now read ...
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Is it absolutely necessary to shuffle a large training data set every epoch?

There have been few questions on shuffling the training dataset every epoch. The general consensus is that it helps so you should do it. My question is whether this gain from shuffling every epoch ...
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2 votes
1 answer
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Does it makes sense to train the model on whole data?

Suppose I am training an lstm model on a stock price data. So for first iteration say I have trained it on 80% of data and then tested it on rest of the 20% data and got the rmse value. Now after this ...
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Why am I getting different prediction result after every run?

I have a simple lstm model ...
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How to add training costs to the variables of a model? [closed]

I am dealing with the Breast Cancer dataset and I want to include costs to the variables, trying to minimize the training costs and maximizing the accuracy. The costs for each variable are as follows <...
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Machine learning problem - train and test with different classes

I have a datasets composed like this: library(caret) data <- iris train <- data [1:75,] test <- data[76:150,] So, I have 3 classes in total but: Train: ...
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How to generate test set with no data-leakage using multiple columns

I am developing a fraud detection algorithm. Among other things, my dataset contains the phone number, email address and a few other fields that should uniquely identify a user (let's call them "...
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Why label encoding before split is data leakage?

I want to ask why Label Encoding before train test split is considered data leakage? From my point of view, it is not. Because, for example, you encode "good" to 2, "neutral" to 1 ...
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What the difference between a flattening validation curve and one that increases again?

I know that we monitor the validation loss to investigate overfitting. I am familiar with the validation curve that first decreases and then increases again. The increasing part means the model starts ...
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Best approach for combining two targets

We have a dataset of user activities and two binary classification models which predicts behavior of users in two relatively similar products. Now we want to have an overall prediction of user ...
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Training error vs testing error for batch gradient descent on Pyrhon, can't understand what I'm supposed to do

I have this dataset containing training data and testing data , and I have to plot training and testing errors for the gradient descent algorithm with square and logistic losses. I'm a beginner, I'm a ...
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how can i set learning rate for big data?

will it need more epochs for training or it is not a necessary and what is the learning rate I should set for this data with optimizer adam?
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Should the model be defined again before training it to new data?

I wanted to fit the LSTM model on new data set in a loop so I have implemented it like this ...
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How to include the sudden peaks/bursts in LSTM based time-series model's training

I am using LSTM for time-series prediction whereby I am taking past 50 values as my input. Now, the thing is that it is predicting just OKish, and not doing the exact prediction, especially for the ...
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How to do modelling for pairs of non i.i.d. data?

I have a dataset in which i have the labels for candidates on whether they would be hired,interviewed_and_failed,not_interviewed_at_all. The task is to predict for new jobs/new candidates what these ...
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How to train/test/validate hierachical classifiers?

I am writing an algorithm which allows to detect activities based on wearable data. I would like to try it out an hierachical approach (Local Classifier Per Parent Node structure). In the first level, ...
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1 vote
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What can be done about mislabeled data points in the training set of a binary classification model?

The training set is exposed to the labels, but what if a portion of these labels were incorrectly labeled by the curator? Is there a way of searching for these training set examples?
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How to address label imbalance in deciding train/test splits?

I'm working on a dataset that isn't split into test and train set by default and I'm a bit concerned about the imbalance between the 'label' distributions between them and how they might affect the ...
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Is it good to use .fit to xtest when we use PolynomialFeatures() of sklearn?

My teacher did this in class, and I'm wondering is this ok to use .fit_transform with xtest? It shouldn't just be poly.transform(xtest) Teacher's Code ...
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Underlying explanation of Neural-Network basedtime-series future-value prediction

I have confusion related to Feed Forward Neural Network. I train my network for time-series prediction, and it is working great and as expected. I know how NNs train and to predict i.e. there is ...
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1 vote
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PowerTransformer Producing Unexpected Result for Just One Column

I'm doing some preprocessing on my training data before fitting it to a model. Upon checking the results, there is one column that is returning 0 rather than 1 for the standard deviation. (all columns ...
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Can we not backpropagate model

I saw a model based on CNN for question classification. The author said that they don't backpropagate gradient to embeddings. How this is possible to update network if you don't backpropagate please? ...
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cuDNN isn't found FWD algo for convolution. How to TRAIN DARKNET ON GE FORCE GTX 1650

ISSUE: while training Darknet with GE FORCE GTX 1650 using following: CUDA 11.0 cuDNN 8.0.5 OPENCV 4.5 Model starts training with config file details as below for [net] section: ...
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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 ...
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Too many hours for Training Custom Object Detector

I am following this tutorial: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html When I reach the training paragraph ( Training the Model ) and run this command: <...
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Model does not learn after ternarization of weights contrary to the paper mentioned below

I’m implementing the ‘Ternary Weights Network’ paper by Fengfu Li and Bo Zhang ( archive link - https://arxiv.org/abs/1605.04711). I’m training a simple Covnet with linear layers on the MNIST dataset. ...
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