Questions tagged [accuracy]
For questions related to the accuracy metric/measure, which is the number of correct predictions divided by the total number of predictions.
62 questions
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In some case, is training from random weight initialization better than fine-tuning?
I just trained my deep learning model with a transformer for a 4-classes multi-classification problem. However, some batches only have 3 classes, making the model biased for those 3 classes and making ...
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Is It Normal for Validation Accuracy to be Higher than Training Accuracy?
I'm training a deep learning model in PyTorch for a classification problem, and I’ve noticed that the validation accuracy is consistently higher than the training accuracy throughout the training ...
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How to improve classification accuracy in TF deep neural network model?
I need help in increasing the accuracy of a classification model using Neural Networks on Tensorflow.
I am trying to train a model on sequential data ...
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Why does the accuracy of my neural network stay constant?
I'm testing my own implementation of a neural network on recognising the type of a function. I generate sine, linear and quadratic functions with random parameters, compute their values for a linspace ...
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Do the True Negative detections in context of Object Detection make the Accuracy metric meaningless?
It seems to me that in context of object detection True Negative "Detections" amount is an infinite number since counting an absence of detected bounding boxes can be described by
an ...
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Efficiency-Focused thesis in Cancer Diagnosis Using AI (Advice Needed)
I'm looking for a topic for my master's thesis, I have an idea about focusing on efficiency in deep learning. I am thinking about investigating different methods (e.g knowledge distillation, pruning, ...
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Why am I getting a difference between training accuracy and accuracy calculated with Keras' predict_classes on a subset of the training data?
I'm trying to solve a binary classification problem with AlexNet. I split the original dataset into training and validation datasets using a 70/30 ratio. I have trained my neural network with a ...
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Combinig output of two different machine learning models for accurate invoice data extraction: Is this a viable approach?
I am working (trying to work) on a project to extract relevant information from invoices. Currently I don't achieve much good accuracy so am trying to come up with some new ideas. I am considering ...
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CNN: Accuracy gap of 5-7 % between accuracy computed on-the-fly and separate model evaluation on the training set
I am training a CNN for some basic classification task. During training, I compute the training accuracy after every epoch. After the training has finished, I evaluate the model again on the entire ...
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What about the loss and custom metric with per-pair weights in multi-class classification?
Let's suppose that we have a multi-class classification problem with 5 classes: 0, 1, 2, 3, 4. The order is not random, they are neighbors. For example, imagine that a labelling is 1. If the ...
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I can’t pass a treshold no matter what I do
I am currently training an CNN for classification. My training data are 80x80 images, 3 channels, which I have grouped into 25% validation, 75% training, all evenly distributed. I have 3 classes into ...
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Does a random forest classifier always get 100% accuracy on its own training data?
Due to the way that decision trees work, do random forest classifiers always get 100% accuracy on their own training data? My random forest classifier got 100% accuracy on its own training data, so I'...
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Improving Neural Network Model Accuracy with Initial Poor Classifications and Clustering
I have a dataset consisting of 52 files, each classified as 'yes' or 'no' for 22 different attributes based on specific subparts, not the entire content. After tokenizing, converting non-generic ...
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Is it possible training accuracy never changed while training?
Question summary
What informations can get from this epoch_accuracy graph?
Is it possible training accuracy never changed like after 10 epoch in graph while training?
Body
I do some experiments with ...
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How to determine alignment / correlation with comparison based judgments?
I have built an automatic metric (using DL) to estimate some property of text. I want to evaluate its performance by comparing it to human judgments with respect to that property. As far as I know ...
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How to check clustering performance?
Background
I'm implementing the DBScan algorithm. I have trained it to cluster a small dataset of random clusters, and want to be able to get a decimal for its accuracy of clustering the groups.
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Which preprocessing is the correct way to forecast time-series data using LSTM?
I just started to study time-series forecasting using RNN.
I have a few months of time series data that was an hour unit.
The data is a kind of percentage value of my little experiment and I would ...
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Is there any different evaluation metrics(Performance Metrics) for Deep learning ,Machine, learning and NLP?
I'm a little confused about machine learning. I know we can use accuracy, and precision-recall when it comes to a classification problem, and when it comes to regression problems, we usually go with ...
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Why does KNN Model return 99% accuracy on dataset with default parameters? [closed]
I am building a model that predicts if a user will like a stock or not based on different features, such as Market Cap, Current Ratio, Sector, Trailing PE, etc. I am going to implement this model in a ...
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Training loss is decreasing very slowly while learning MNIST database
I am developing my ANN from scratch which is supposed to classify MNIST database of handwritten digits (0-9). My feed-forward fully connected ANN has to be composed of:
One input layer, with ...
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Very low accuracy (0.11) and it remains constant after few epochs on MNIST database
I am developing my ANN from scratch which is supposed to classify MNIST database of handwritten digits (0-9). My feed-forward fully connected ANN has to be composed of:
One input layer, with ...
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Is the neural network 100% accurate on training data if epoch loss is minimized to 0?
This seems like a silly, trivial question, but I just want to confirm it in case I'm missing something.
I'm trying to train a ReLU neural network, which is supposed to be a function that satisfies ...
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Is the accuracy the best metrics to evaluate the performance of Deep Learning model? [closed]
Consider a model A that achieved an test accuracy of 99% on dataset-A with the size of 200 images and a model B that achieved ...
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What is the relationship between the training accuracy and validation accuracy?
During model training, I noticed various behaviour in between training and validation accuracy. I understand that 'The training set is used to train the model, while the validation set is only used to ...
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Where can I find authentic references on "categorical cross entropy" and "categorical accuracy metric"?
My Python source code uses TensorFlow and Keras to implement a neural network.
The Keras source code uses something called "categorical cross-entropy" and "categorical accuracy metric&...
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Confusion Matrix Measures vs Accuracy level in Neural Network Model
I'm working on a classification machine learning problem with two classes: high and low, which are derived from another numerical column x. Previously, if x>100, the sample is considered ...
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Given the precision and recall of this model, what can I say about it?
The following table shows the precision and recall values I obtained for three object detection models.
The goal is to find the best object detection model for that particular data set.
I evaluate ...
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Effect of batch size and number of GPUs on model accuracy
I have a data set that was split using a fixed random seed and I am going to use 80% of the data for training and the rest for validation.
Here are my GPU and batch size configurations
use ...
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Should I choose the model with highest validation accuracy or the model with highest mean of training and validation accuracy?
I'm training a deep network in Keras on some images for a binary classification (I have around 12K images). Once in a while, I collect some false positives and add ...
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Is it ok to have an accuracy of 65% and a sensitivity of 90% with Naive Bayes for sentiment analysis?
I am creating a sentiment analysis model using Naive Bayes. When I test the model, I get an average accuracy of 65%; however, the sensitivity of the model is much higher, 90%.
So, I am wondering if ...
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Test accuracy decreases during my train process
I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and ...
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Is a test accuracy of 0.74 good enough, given a dataset of about 700 samples, and, if not, how can I improve it?
I am new to neural networks. I am trying to solve a binary classification problem. Specifically, I want to determine whether a patient has or not a certain disease based on the dataset.
The dataset ...
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Accuracy dropped when I ran the program the second time
I was following a tutorial about Feed-Forward Networks and wrote this code for a simple FFN :
...
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Why is my test error lower than the training error?
I am trying to train a CNN regression model using the ADAM optimizer, dropout and weight decay.
My test accuracy is better than training accuracy. But, as far as I know, usually, the training accuracy ...
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Should I continue training if the neural network attains 100% training accuracy?
I have a neural network where there are two hidden layers. Each hidden layer has 128 neurons. The input layer has 20 inputs, and the output layer has 3 outputs.
I have 1 million records of data. 80% ...
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What is the difference between the definition of "accuracy" in machine learning and federated learning?
What is the difference between the definition of "accuracy" in machine learning and federated learning?
In particular, how is the accuracy calculated in the following paper:
Cai, Lingshuang,...
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Is it a good practice to pad signal before feature extraction?
Is padding, before feature extraction with VGGish, a good practice?
Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in ...
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Do larger numbers of hidden layers have a bigger effect on a classification model's accuracy?
I trained different classification models using Keras with different numbers of hidden layers and the same number of neurons in each layer. What I found was the accuracy of the models decreased as the ...
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Could I just choose the other (non-predicted) class when the accuracy is low?
I have a binary classification problem.
My neural network is getting between 10% and 45% accuracy on the validation set and 80% on the training set. Now, if I have a 10% accuracy and I just take the ...
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Accuracy Not Going Above 30%
I am trying to make a big classification model using the coco2017 dataset. Here is my code:
...
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Is it possible that the model is overfitting when the training and validation accuracy increase?
I am aware of similar questions that have been asked, and I have gone through many. I want to bring my case to SE to understand better what my results are.
I am working with a large dataset (around ...
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What are possible ways to combat overfitting or improve the test accuracy in my case?
I have asked a question here, and one of the comments suggested that this is a case of severe overfitting. I made a neural network, which uses residual boosting (which is done via a KNN), and I am ...
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Why is my siamese network learning very well in e.g. 1 out of every 5 runs?
Why is my siamese network learning very well in e.g. 1 out of every 5 runs? The rest of the time it's not learning and maintains an accuracy of 0.5.
Any explanations? Is the contrastive loss taken in ...
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Why would my neural network have either an accuracy of 90% or 10% on the validation data, given a random initialization?
I'm making a custom neural network framework (in C++, if that is of any help). When I train the model on MNIST, depending on how happy the network is feeling, it'll give me either 90%+ accuracy, or ...
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Is there a way, while training (with contrastive learning) the embedding network, to find the test accuracy?
I aim to do action recognition in videos on a private dataset.
To compare with the existing state-of-the-art implementations, other guys published their code on Github, like the one here (for the ...
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How to express accuracy of a regression ANN that uses MSE loss function?
I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the '...
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Why don't people always use TensorFlow Lite, if it doesn't decrease the accuracy of the models?
I have been exploring edge computation for AI, and I came across multiple libraries or frameworks, which can help to convert the model into a lite format, which is suitable for edge devices.
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Should I choose a model with the smallest loss or highest accuracy?
I have two Machine Learning models (I use LSTM) that have a different result on the validation set (~100 samples data):
Model A: Accuracy: ~91%, Loss: ~0.01
Model B: Accuracy: ~83%, Loss: ~0.003
The ...
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What is the effect of too harsh regularization?
While training a CNN model, I used an l1_l2 regularization (i.e. I applied both $L_1$ and $L_2$ regularization) on the final layers. While training, I saw the ...
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Why do I get higher average dice accuracy for less data
I am working on image segmentation of MRI thigh images with deep learning (Unet). I noticed that I get a higher average dice accuracy over my predicted masks if I have less samples in the test data ...
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