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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.

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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'...
user1181399's user avatar
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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 ...
Ferda-Ozdemir-Sonmez's user avatar
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1 answer
60 views

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 ...
Yang's user avatar
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How is the accuracy as a metric in a Keras machine learning model calculated? Is it a valuable metric for LSTM

I'm training a LSTM neural network for time series prediction in Keras. During the training of the model, the loss (mse) gradually decreases each epoch, but the accuracy as well as the validation ...
Mappy's user avatar
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0 answers
23 views

High Accuracy ML.NET Image differentiation model

I have a relatively big dataset (100+GB) that has 35 categories. All of them are microscopic images with slight differences. Although ML.NET documentation itself declares that training time should be ...
Helios Lucifer's user avatar
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25 views

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. ...
SamTheProgrammer's user avatar
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1 answer
2k views

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 ...
orde.r's user avatar
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2 answers
300 views

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 ...
Šimon Bláha's user avatar
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1 answer
45 views

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 ...
Fr_nkenstien's user avatar
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1 answer
43 views

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 ...
Osmosis D. Jones's user avatar
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18 views

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 ...
Shreneek Upadhye's user avatar
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1 answer
265 views

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 ...
Messi10's user avatar
1 vote
0 answers
95 views

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 ...
Anna's user avatar
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157 views

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 ...
tail's user avatar
  • 157
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0 answers
43 views

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 ...
Acad's user avatar
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2 answers
71 views

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 ...
Dilip C M Dept of MCA's user avatar
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1 answer
258 views

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&...
user366312's user avatar
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1 answer
137 views

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 ...
nilsinelabore's user avatar
1 vote
1 answer
693 views

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 ...
klayoe's user avatar
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0 answers
406 views

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 ...
FateMintTrio's user avatar
2 votes
1 answer
2k views

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% ...
user366312's user avatar
0 votes
1 answer
169 views

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,...
Jared's user avatar
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1 answer
141 views

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 ...
Sebastian's user avatar
1 vote
1 answer
99 views

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 ...
Dawid_K's user avatar
  • 13
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2 answers
967 views

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 ...
Shonix3373's user avatar
0 votes
2 answers
569 views

Accuracy Not Going Above 30%

I am trying to make a big classification model using the coco2017 dataset. Here is my code: ...
DragonflyRobotics's user avatar
0 votes
1 answer
234 views

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 ...
nachofest's user avatar
0 votes
1 answer
92 views

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 ...
jr123456jr987654321's user avatar
0 votes
2 answers
94 views

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 ...
jr123456jr987654321's user avatar
0 votes
0 answers
42 views

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 ...
Ari K's user avatar
  • 111
1 vote
0 answers
102 views

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 ...
krishna chaitanya's user avatar
0 votes
0 answers
219 views

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 ...
Ilknur Mustafa's user avatar
0 votes
0 answers
41 views

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 ...
Sepehr Golestanian's user avatar
2 votes
0 answers
88 views

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 ...
Lis Louise's user avatar
1 vote
1 answer
483 views

Why does the accuracy drop while the loss decrease, as the number of epochs increases?

I've been trying to find the optimal number of epochs that I should train my neural network (that I just implemented) for. The visualizations below show the neural network being run with a variable ...
eGood's user avatar
  • 11
1 vote
0 answers
53 views

Why does loss and accuracy for a multi label classification ann does not change overtime?

I have run into a strange behavior of my multi label classification ANN ...
User2741's user avatar
1 vote
0 answers
206 views

Keras model accuracy not improving beyond threshold

I am currently working on a public project for the National Weather Model. We are experimenting with using a recurrent neural network to replace the output of a quadratic formula that is in use. The ...
Jacob Hreha - NOAA Affiliate's user avatar
0 votes
1 answer
1k views

Which kind of data does sigmoid kernel performance well?

While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
Gooday2die's user avatar
1 vote
0 answers
39 views

Low accuracy during training for text summarization

I am trying to implement an extractive text summarization model. I am using keras and tensorflow. I have used bert sentence embeddings and the embeddings are fed into an LSTM layer and then to a Dense ...
inquisitive's user avatar
1 vote
0 answers
72 views

Why is the accuracy of my model very low on a separate dataset from the training and test datasets?

I am working on stock price prediction project, I am using the support vector regression (SVR) model for it. As I am splitting my data into train and test, I am getting high accuracy while predicting ...
Debugger's user avatar
3 votes
1 answer
101 views

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 : ...
Eeshaan Jain's user avatar
2 votes
1 answer
735 views

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 ...
Saha's user avatar
  • 131
3 votes
1 answer
416 views

Why is there more than one way of calculating the accuracy?

Some sources consider the true negatives (TN) when computing the accuracy, while some don't. Source 1: https://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-...
Stephen Philip's user avatar
1 vote
1 answer
102 views

Choosing Data Augmentation smartly for different application

I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is fire detection (on video frames), with almost 15K ...
Mary's user avatar
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2 votes
2 answers
1k views

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 ...
Mary's user avatar
  • 973
1 vote
1 answer
88 views

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 ...
Nilani Algiriyage's user avatar
8 votes
2 answers
10k views

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 ...
bit_scientist's user avatar
1 vote
1 answer
104 views

Can we calculate mean recall and precision

I'm evaluating the accuracy in detecting objects for my image data set using three deep learning algorithms. I have selected a sample of 30 images. To measure the accuracy, I manually count the number ...
Nilani Algiriyage's user avatar
1 vote
0 answers
40 views

Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
Nilani Algiriyage's user avatar
1 vote
1 answer
149 views

How to explain peak in training history of a convolutional neural network?

I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using: ...
Cristian M's user avatar