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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resnet50 based model classification loss increase

I'm trying to classify fonts in images into 7 classes. I wanted to use a pre-trained ResNet50 for the task and use its features to my classification. So I've followed some guide and came up with the ...
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81 views

Validation loss and validation accuracy stay the same in NN model

I am trying to train a keras NN regression model for music emotion prediction from audio features. (I am a beginner in NN and I am doing this as study project.) I have 193 features for training/...
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Comparing two models with different (naive) baseline

I would like to compare a model with listwise deletion to a model with multiple imputation. However, the model with listwise deletion has a majority class of 70%, while the model with multiple ...
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35 views

How to calculate a single accuracy for a model with multiple outputs in Keras?

Consider the following, rather simple, model: ...
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1answer
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Accuracy after selftraining didn't change

I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would ...
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2answers
205 views

AUC ROC Threshold Setting in heavy imbalance

I am doing binary logistic regression on a dataset with very heavy class imbalance. Class 1 is only 1% of data. When I train logistic regressor without class weights I get ROC AUC Score of 0.6269. ...
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Why are the Order Of Initial Centroids effecting Kmeans Clustering?

For Iris Dataset I am doing the experiment. iris_k_mean_model_vor = KMeans(n_clusters=3, init=arr_4d) this is my model. Here I am feeding an Initial array of ...
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1answer
1k views

Metrics for presenting RNN/LSTM result

I am working on two different architectures based on the LSTM model to predict the user's next action based on the previous actions. I am wondering, what is the best way to present the result? Is it ...
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2answers
291 views

Same validation accuracy, different train accuracy for two neural networks models

I'm performing emotion classification over FER2013 dataset. I'm trying to measure different models performance, and when I checked ImageDataGenerator with a model I had already used I came up with the ...
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2answers
514 views

Generalization of accuracy score based on subset of data points

I have a multi-class problem that I am building a classifier for. I have N total data points I would like to predict on. If I instead predict on n < N data points and get an accuracy score, is ...
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how to train image classification with small difference in low-level feature

I would like to build the model that capable of doing classification for example 2 classes below : I tried alexnet, resnet50, resnet18, vgg16 but seem they are failed to differentiate between this ...
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Time Series Hyperparameter Tuning

My question is about the intuition for hyperparameter tuning of time series. In other models, like Linear or Logistic Regression there is labeled data and according to accuracy or precision, the ...
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2answers
177 views

Adding extra variables to XGboost model is worsening the train and test accuracy

I am fitting a multi class model using Xgboost. I am getting an accuracy of 96% on Train and 95% on test. I am using the 80-20 train/test split. However, when I am adding two new features , the ...
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evaluation metrics for multiple values per session

I have an application that executes my foo() function several times for each user session. There are 2 alternate algorithms that i can implement as "foo" function and my goal is to evaluate them based ...
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Validation accuracy have an almost good accuracy but loss function is high too

Based on my project, I find a little problem there with the statement like this. I want to make model with neural networks for text dataset. Then I use Pad Sequence for my text and Array for the ...
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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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312 views

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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2answers
920 views

Manual way to draw accuracy/loss graphs

During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below: ...
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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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534 views

Keras: extreme spike in loss during training

I am training an LSTM for time series forecasting and it has produced an extremly high loss value during one epoch: ...
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How can i adapt accuracy metric for multiclass classification?

I have a problem which is multiclass e.g. That is 4 classes. I would like a custom metric to assess the model where only if class 3 is predicted as class 2 and class 2 is predicted as class 3 (i.e. ...
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1answer
36 views

Query regarding surprising spike in accuracy of ML model

I implemented all the major ML models (Logistic Regression, Naive Bayes, SVM, KNN, Decision Tree, Random Forest, Ada Boost & XGBoost) on my dataset. My stratified cross-validation scores are ...
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1answer
59 views

what are the next step after ML prediction and how to proceed?

I have trained an ML model with a good accuracy but what next? I am facing difficulty in answering this question, how will you present your model. Which framework do you use How do you make sure ...
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1answer
55 views

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 to make an DL model predict Correctly [closed]

So I trained a DL algorithm using Keras for Human Action Recognition. The model has an accuracy of about 85 percent and a loss of 0.3 something. The problem is that the model did not predict well on ...
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1answer
156 views

My accuracy changes throughout every epoc but the val_acc at the end of each epoc stays the same

I am training a transfer learning CNN on 161 pictures that have been augmented into 966 photos, 4 classes. I am training on a balanced data set, so there are 52 images of each class and also in the ...
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1answer
641 views

test accuracy of text classification is too less

I have a data set of movies and their subtitles. My task is to classify them based on their ratings - [R, NR, PG, PG-13, G]. I have 13 examples for each class. I preprocessed the subtitles in the ...
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After finalizing the hyper-parameters and model using a val set, is it recommended to train the final model using both train and validation sets?

Assume I have all the 3 pre-standardized splits of a dataset (train, val, set), with groundtruth for all 3. Usually many datasets are of this form. Once I have finalized the best configuration of ...
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40 views

val_sparse_categorical_accuracy

I know the metric sparse_categorical_accuracy ...
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3answers
999 views

Accuracy is lower than f1-score for imbalanced data

For a binary classification, I have a dataset with 55% negative label and 45% positive labels. The results of the classifier shows that the accuracy is lower than the f1-score. Does that mean that the ...
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2answers
311 views

Tensorflow keras fit - accuracy and loss both increasing drastically

ubuntu - 20.04 tensorflow 2.2 dataset used = MNIST I am testing tensorflow and i notice that validation sparse_categorical_accuracy (accuracy) and validation <...
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Model accuracy and validation accuracy stuck at constant value for denoising autoencoder model

I am building a denoising autoencoder (DAE) to denoise respiratory signals. I pass through the model both noisy and clean versions of the signal (in frame sizes as multiples of 1024). I've set up my ...
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1answer
59 views

Optimize F-Score only for certain classes, disregard other classes

I have a labeled dataset of product reviews where the label is a rating between 1 and 5 and the review is just text. I use a simple naive Bayes classifier (sklearn) to try to predict a rating given a ...
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1answer
41 views

Compare model accuracy when training with imbalanced and balanced data

So I was recently doing a data science project which is a multi class classification. The project can be found https://www.kaggle.com/c/otto-group-product-classification-challenge. The dataset is an ...
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3k views

How to fix "Expected sequence or array-like"

I am trying to get the accuracy of the model and I am getting this error TypeError: Expected sequence or array-like, got Here's my code sample. ...
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3k views

human level performance on ImageNet, top-1 or top-5?

Anyone have pointers to where the human level performance on ImageNet comes from? I found a reference to 5.1% accuracy (top-1? or top-5?) from here.
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51 views

Spliting Training Test and Validation for Image Dataset

I have 600 images in the training folder, 200 images in the validation folder, and 200 images in the test folder. Suppose if I fit the training data generator and validation data generator for some ...
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36 views

Improve model accuracy in multi-classification problem

I use a MLP to classify three different classes A, B, C. The loss function I use is categorical cross entropy and the optimiser ...
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1answer
150 views

How to get the number of steps until a certain accuracy in keras?

I want to see how many steps does it take for my model to reach a certain accuracy.Say 90 percent on cifar10.How can I get this info from the keras model ? EDIT: accuracy in each epoch is accessible ...
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How to increase model's test accuracy?

I am using the InceptionV3 model for training. Here is the link for the code (https://github.com/maxmelnick/tensorflow/blob/no_random/tensorflow/examples/image_retraining/retrain.py) Initially I have ...
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1answer
20 views

Advantages to combining similarly-named columns for supervised ML?

Is there any benefit to combining similarly named columns either for an improvement in accuracy or for speeding up training/prediction in case of logistic regression, random forest or neural network ...
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2answers
38 views

Understanding Sklearns learning_curve

I have been using sklearns learning_curve , and there are a few questions I have that are not answered by the documentation(see also here and here), as well as questions that are raised by the ...
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1answer
63 views

I am getting very minimal mse values and not sure if it is correct?

Below is the linear regression model I fitted and not sure if I am doing the right way as I am getting neat to 99% accuracy Fitting Simple Linear Regression to the Training set ...
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1answer
31 views

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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2answers
22 views

Multiple models have extreme differences during evaluation

My dataset has about 100k entries, 6 features, and the label is simple binary classification (about 65% zeros, 35% ones). When I train my dataset on different models: random forest, decision tree, ...
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How can I calculate the level of agreement between my K-Means cluster labels and my ground truth labels in R?

I have made a K-Means clustering from 3 rasters with various values of k (k=2, k=4, k=7) and would like to know which values of k explains the most variance in my ground-truth data or the value of k ...
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2answers
383 views

Unable to understand the usage of labels argument in sklearn.metrics.f1_score

I am trying to model a dataset with RandomForest Classifier. My dataset has 3 classes viz. A, B, C. 'A' is the negative class ...
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1answer
27 views

Comparing accuracies of Grid Search CV & Randomized Search CV with K-Fold Cross Validation?

Are Grid Search CV & Randomized Search CV always/necessarily supposed to give more accurate results after hyperparameter tuning as compared to K-Fold Cross Validation?
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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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