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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Is it always better to use the whole dataset to train the final model?
A common technique after training, validating and testing the Machine Learning model of preference is to use the complete dataset, including the testing subset, to train a final model to deploy it on, ...
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What would I prefer - an over-fitted model or a less accurate model?
Let's say we have two models trained. And let's say we are looking for good accuracy.
The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted.
The second has an ...
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Training an RNN with examples of different lengths in Keras
I am trying to get started learning about RNNs and I'm using Keras. I understand the basic premise of vanilla RNN and LSTM layers, but I'm having trouble understanding a certain technical point for ...
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Train, test split of unbalanced dataset classification
I have a model that does binary classification.
My dataset is highly unbalanced, so I thought that I should balance it by undersampling before I train the model. So balance the dataset and then ...
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How to fix class imbalance in training sample?
I was very recently asked in a job interview about solutions to fix an imbalance of classes in the training dataset. Let's focus on a binary classification case.
I offered two solutions: oversampling ...
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Why an increasing validation loss and validation accuracy signifies overfitting?
When I train a neural network, I observe an increasing validation loss, while at the same time, the validation accuracy is also increased.
I have read explanations related to the phenomenon, and it ...
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How data are prepared during training, testing and in production?
Most of real world datasets have features with missing values. Replacing missing values with an appropriate value such as its mean, is considered as a good step in feature engineering. Some times we ...
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Should a model be re-trained if new observations are available?
So, I have not been able to find any literature on this subject but it seems like something worth giving a thought:
What are the best practices in model training and optimization if new observations ...
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In the context of Deep Learning, what is training warmup steps
I found the term "training warmup steps" in some of the papers. What exactly does this term mean? Has it got anything to do with "learning rate"? If so, how does it affect it?
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Oversampling/Undersampling only train set only or both train and validation set
I am working on a dataset with class imbalance problem. Now, I know one needs to oversample or undersample only the train set and not the test set. But my issue is: whether to oversample the train set ...
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Train object detection without annotated data/bounding boxes
From what I can see most object detection NNs (Fast(er) R-CNN, YOLO etc) are trained on data including ...
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Build a tool for manually classifying training data images
I have a large number of images that I need to classify for training a clustering algorithm, and I would like to do so offline (the data is proprietary). Basically, I'd like to build a desktop survey ...
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How to train data by batch from disk?
I am working on a convolutional neural network for image classification. The training dataset is too large to be loaded on my computer memory (4gb), on top of that I also need to try some augmentation ...
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CNN for phoneme recognition
I am currently studying this paper, in which CNN is applied for phoneme recognition using visual representation of log mel filter banks, and limited weight sharing scheme.
The visualisation of log ...
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Tool to Label Images for Supervised Classification
I have a couple thousand photos of whales taken from drones and I'm planning to build a simple binary classifier to run on these and future images to see if they contain a whale. I'd like to label ...
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How to predict based on multiple samples?
I am relatively new to ML so I apologies in advance if my question shows lack of understating of the field.
The problem
A particular study course has a high drop-out rate and we want to reduce it.
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Is it correct to join training and validation set before inferring on test-set?
I would like to know if is a correct procedure to join training-set and validation-set together, in order to train the model on ...
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DC GAN with Batch Normalization not working
I'm trying to implement DC GAN as they have described in the paper. Specifically, they mention the below points
Use strided convolutions instead of pooling or upsampling layers.
Use only one fully ...
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Understanding Weighted learning in Ensemble Classifiers
I'm currently studying Boosting techniques in Machine Learning and I happened to understand that in Algorithms like Adaboost, each of the training samples is given a weight depending on whether it was ...
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"other" class in Image classification
In the MNIST dataset, you have 10 defined classes, one for each digit. But you don't have a "not a digit" class. It seems that most image classification datasets are the same. But in a business ...
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When forecasting time series, how does one incorporate the test data back into the model after training?
When you build a classification or regression model, you typically split the data into a train data set and a test data set. The test data is a randomly selected subset of the overall data.
Once you ...
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Why can't I choose my hyper-parameter in the training set?
Say I've divided the data into 3 parts: training, validation and test. I know for example, that in Neural Networks, the number of hidden layers is a hyper parameter. Why can't I train numerous NN ...
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What is the next step after k fold CV?
I came across this video lecture https://www.youtube.com/watch?v=wjILv3-UGM8
on k fold cross validation (CV). The algorithm given in the video lecture is presented below:
for k = 1:5
train on all ...
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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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Can a classifier be trained with reinforcement learning without access to single classification results?
Question: Can a classifier be trained with reinforcement learning without access to single classification results?
I want to train a classifier using reinforcement learning. However, there is one big ...
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When a dataset is huge, what do you do to train with all the images on i t?
I'm using Python 3.7.7.
I'm trying to load a lot of NIFTI images using SimplyITK and Numpy from the [BraTS 2019 dataset][1].
This is the code I use to load the images into a numpy array.
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Does keras' model.fit() remember learning rate when called multiple times?
Let's say I'm using the Adam optimizer, and calling fit() on my model multiple times. What parameters does the fit function remember?
From what I've observed, the loss function/metrics seem to ...
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Training a neural network with TWO possible correct outputs for one input
I have a system as a black box that has two correct outputs for a single input sample.
now I want to train a neural network to generate at least one of the correct outputs for that input sample.
what ...
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Dataset and why use evaluate()?
I am starting in Machine Learning, and I have doubts about some concepts. I've read we need to split our dataset into training, validation and test sets. I'll ask four questions related to them.
1 - ...
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How to resolve the instability of average reward per episode in training of DQN (Deep Q-Network)?
what is shown when average reward per episode in training is unstable?
If there is big difference between average reward per episode and final reward by test section, what we can say?
For instance in ...
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How to train a model using a daunting huge training dataset
I have an extremely huge dataset and I'm wondering me how could be the right way to set an experiment to use this data to train a model.
I understand that I can use data-reduction to, for instance, ...
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Time series data and ML - separating training/test data
I am using XGBoost to try to predict the direction of the stock market based on social media sentiment. Having read through some studies, I was planning to separate the training/test data by time ...
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is validation and train set should be all different files?
Let say I have train set and validation set
if 'A' included in the train set. 'A' should not be include in the validation set? or some is ok?
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Is a test set necessary after cross validation on training set?
I'd like to cite a paragraph from the book Hands On Machine Learning with Scikit Learn and TensorFlow by Aurelien Geron regarding evaluating on a final test set after hyperparameter tuning on the ...
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TF object detection - The total number of detected objects is not increasing
I'm building a model to recognize fishes in the aquarium (150 different fishes).
I'm using a faster_rcnn_inception_v2_coco_2018_01_28 model for transfer learning from TF object detection API. I have ...
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Present results of the best or the last iteration on dev set?
Which is the correct way - presenting the results of the best or the last iteration on the dev set in a paper? In research papers I usually see only one value, is it the best iteration of all?
I'm ...
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