Questions tagged [accuracy]

In data science, accuracy is a measurement used to determine which model is best at describing the underlying patterns of a dataset.

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Which neural network is better?

MNIST dataset with 60 000 training samples and 10 000 test samples. Neural network #1. Accuracy on the training set: 99.53%. Accuracy on the test set: 99.31%. Neural network #2. Accuracy on the ...
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Is it ok for precision and recall metrics if a small minority of samples are both false positives and true positives?

I am working on a multi-label classification NN using genomic data. there are 10 samples and 2 ground truth labels (age and gender) for every sample. I use a sigmoid activation at the final layer and ...
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21 views

Evaluating optimal values for depth of tree

I'm studying the performance of an AdaBoost model and I wonder how it performs in regard to the depth of the trees. Here's the accuracy for the model with a depth of 1 and here with a depth of 3 ...
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Confusion matrix and accuracy not in sync

I am getting the following result for the confusion matrix and accuracy for a logistic regression model. ...
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Standard datasets for Classical Machine Learning tasks

I'm aware of and have worked with many datasets in Classical ML as well as DL. I am also aware of some of the standard datasets in DL (for example ImageNet for Image Classification, etc.) However, I ...
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Q. Why is my testing accuracy varying slightly with batch size (97.7% - 100%)?

As you will see, when batch size is set to 1, I'm consistently getting 97.7% testing accuracy for all 10 iterations. However, when batch size is set to 64, I'm getting a testing accuracy of 100% 7 out ...
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Why won't my TFJS model's accuracy exceed .508 despite loss decreasing, and the fact that it worked for a different dataset (Iris dataset)?

This post is aptly titled: my stock prediction model's accuracy just won't go past 0.5088282227516174 despite loss decreasing. I have tried so many different things, such as: Increasing batch size ...
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48 views

What metric should I use to achieve perfect score when choosing all possible results?

A guy told me that he can predict which player I would choose from Greece's Euro 2004 Champion football team. Assume my choice was random. He then goes ahead and names all the players of the team. He ...
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What do you suggest to increase the sensitivity rates in conventional ML model?

I have an imbalanced data problem (prop. rate: 0.8571429 0.1428571) and for this reason, our sensitivity and PPV rates are very low. What do you recommend to fix this problem in R or in general? See ...
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Evaluation Metrics for Multiclass

How to obtain the Accuracy, Detection_Rate, False_Positive_Rate and ...
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How to obtain Accuracy of Feature Selection methods?

I used the following methods: Variance_Threshold: selecto_vth = VarianceThreshold(threshold=1.0) ANOVA: ...
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How to increase the Accuracy after Oversampling?

The Accuracy before ovesampling : On Training : 98,54% On Testing : 98,21% The Accuracy <...
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How to choose the right threshold for binary classification?

I am currently working on the titanic dataset from Kaggle. The data set is imbalanced with almost 61.5 % negative and 38.5 positive class. I divided my training dataset into 85% train and 15% ...
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How to evaluate the performance of CNN model when val_acc changes everytime I ran the code?

I have a CNN model and am trying different feature extraction techniques on my data and passing it to my CNN model in TensorFlow. Let's say I chose SIFT as my feature extraction technique and trained ...
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Is my CNN model overfitting or underfitting?

I would like to be sure of whether the model is overfitting or undercutting. Being new to this, is there any specific point to identify when to stop the training process. Any help in this regard would ...
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Calculating confidence interval for model accuracy in a multi-class classification problem

In the book Applied Predictive Modeling by Max Kuhn and Kjell Johnson, there is an exercise concerning the calculation of a confidence interval for model accuracy. It reads as follows. ...
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Why does adding data augmentation decrease training accuracy a tiny bit?

Before data augmentation, my model clearly overfits and hits a 100% training accuracy and a 52% validation accuracy. When only adding data augmentation with Keras, as a regularization technique, it ...
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Good model but bad confusion matrix?

I am trying to understand the code here. The output [12] shows that the model accuracy is above 90% even for the validation set, but the confusion matrix in [16] ca not even achieve 50% accuracy, and ...
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50 views

AUC higher than accuracy in multi-class problem

I stumbled upon a 3-class classification problem where all compared classifiers yield a higher AUC than accuracy (usually around 10% higher). This happens both when the dataset is balanced or slightly ...
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Model accuracy when training on GPU and then inferencing on CPU

When we are concerned about speed, GPU is way better than CPU. But if I train a model on a GPU and then deploy the same trained model (no quantization techniques used) on a CPU, will this affect the ...
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How to improve model accuracy by redistributing training data over test, validation, and training data subsets after training

How do I improve model accuracy by redistributing training data over test, validation, and training data subsets after training, and then training a new model with the updated data subsets. The model ...
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1answer
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Autoencoder train and test accuracy shooting to 99% on few epochs

I am trying to train an autoencoder for dimensionality reduction and hopefully for anomaly detection. My data specifications are as follows. Unlabeled 1 million data points 9 features I am trying to ...
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How does fusing operations lower accuracy for machine learning models?

In this talk the speaker Sachin Joglekar mentions that it's important to consider tradeoffs when choosing delegates for optimizing Tensorflow Lite. One of the tradeoffs he mentions at 10:14 is that ...
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Improving a CNNs accuracy - help & advice

So I have created a CNN for image classification, and I train and test it with two datasets. One contains 9,339 images and the other 9,100 images. The first model which I designed gave an accuracy of ...
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Validation Accuracy, Validation Loss and Training Loss Remain Constant

Background Hello, I'm new to deep learning and I recently trained a simple convolutional neural network from Francois Chollet's Deep Learning with Python book. The network was trained on 12500 images ...
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CNN model accuracy

I have trained my CNN model on CIFAR 10 and I got val_accuracy of 87% which is not a low value but when it comes to detection of pictures my model detected most of the pictures wrong. anyone knows why ...
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38 views

CNN model low accuracy

I have 1299 images in 4 classes (374/269/284/372). I want to use the VGG19 model, add a dense layer at the top and fine-tune it with my images. As I only have 1299 images, I also want to use data ...
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Train and Validation Curve

I'm new in DeepLearning. I'm not good at understanding and commenting on graphics.Can you help me with these graphs
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How to explain a relationship between Accuracy and F1 Score / F-Measure?

I am building a CNN model for pitch estimation using a song recording. Pitch estimation is done by inputting spectrogram to CNN model and make the CNN predict pitch sequence (250 pitch values per ...
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Metrics for each class in a multi-class training/testing dataset

I am working with the NSL_KDD Dataset for cyber security and training a number of different models. There are five different classes: benign, dos, probe, r2u, and u2l. (I am using scikitlearn.) I ...
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Good approach to increase accuracy for a continuous value that is highly variable/sensitive to the inputs?

I am trying to predict a continuous 'Y' variable using a variety of algorithms and feature engineering techniques. My issue is that Y is extremely variable and I reached a asymptote in accuracy. This ...
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38 views

How to compute f1_score for multiclass multilabel classification

I have used one hot encoder [1,0,0][0,1,0][0,0,1] for my functional classification model. The predicted probabilities for test data ...
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Random forest: Should OOB accuracy equals Test Set accuracy?

In random forests, we bootstrap a sample from the training set, and train a decision tree on the bootstrapped sample. Some observations are not drawn from the training set, and not used to train the ...
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Training loss = 0, training accuracy =1, validation and test around 85%

I have created different CNNs for doing image classification. The dataset is this: https://www.kaggle.com/crowww/a-large-scale-fish-dataset There are 9 classes, and each class contains 1000 images of ...
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Weighted accuracy, sensitivity and specificity

I have a confusion matrix TN= 27 FP=20 FN =11 TP=6 I want to calculate the weighted average for accuracy, sensitivity and specificity. I know the equation but unsure how to do the weighted averages.
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Validation accuracy does not increase for binary classification using GNN

I am trying to perform graph classification with GINConv model of GNN. I have tried everything from varying dropouts to weight decay (for L2 regularization), learning rate (1e-6 to 1e-3), batch ...
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How do I calculate the accuracy for graph mining in terms of (top 1%)?

I have 3600 samples in my dataset. I split the dataset into the train (2700) and test (900). My problem is related to ...
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Compute similiarty between labels

I have a labeled dataset and I created a duplicate of this dataset and removed the labels and applied K-means clustering with k= the number of labels in the original data set I want to compute ...
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How to identify the feature that make the model misclassifed in text classification

Hi I am working on social media financial THAI text classification, the problem with this one is the confused classes, the misclassified prediction has a pattern that consistent as a pair. and I want ...
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What is a reasonable standard deviation in accuracy between different train-test splits?

My model's performance depends on the train-test-split performed. I did 1000 train-test splits and had an average accuracy of 75.4 % and the accuracy had a standard deviation of 2.4 % over those 1000 ...
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LSTM Train accuracy decreases in training time during the epoch

I'm training an LSTM model for sentiment analysis on a text corpus. There's a thing that I believe is not normal because I never have seen it in training my models. At the start of the epoch, the ...
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228 views

How to interpret training and testing accuracy which are almost the same?

Note - I have read this post but still don't understand I have a Naive Bayes classifier, when I input my training data to test the accuracy, I get 63.05%. When I input my test data, the accuracy is 65....
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How to increase accuracy and decrease loss of my model

https://jovian.ai/casella0798/badmodel I created the model above to predict red wine quality. I have 6 classes, from 3 to 8. Dataset is unbalanced, with a lot of classes 5 and 6. My model performs ...
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Test set larger than train set [closed]

There is a two class dataset with 1121 values in total, having 230 from same class and 891 from the other class. The training set is choosen as 230+230=460 from both classes and the test set as the ...
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Is 50% accuracy on a 4 label multiclass classifier good?

As the title says, I have a classifier with 4 labels. I am having trouble getting much above 50% accuracy in Predicting labels. I made sure the data and test sets are made up of approximately 25% of ...
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How to calculate separate binary accuracy metric for each label in a multi-label classification in Keras?

I'm stuck with this: def multilabel_binary_acc(y_true, y_pred): return [K.equal(y_true[:,n], K.round(y_pred[:,n])) for n in range(y_pred.shape[1])] But it ...
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different scored probability distributions for a classification model using python vs Azure ML Studio

I have a classification model which I initially built in Azure ML studio and then created a similar model in python (its similar because the algorithms in python VS Azure are a bit different so they ...
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355 views

sklearn “balanced_accuracy_score” sample_weights not working

I wanted a metric where I could weigh each class as I wish while measuring "total accuracy". sklearn seems to have this with ...
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Why are the Loss and Accuracy metrics not suitable?

I have read in a book where it was described that Loss and Accuracy are only conditionally suitable for a statement strength. Other metrics are used like Precision, Recall, ... . Does anyone know a ...
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Why there is Different accuracy for two same trained model?

Trained the same model twice with the same dataset, the same parameters (Epochs, Batch Size, Learning rate, etc..). But both trained model shows different train as well as test accuracy on the same ...

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