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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Accuracy is decreased, and loss is not changing for logistic regression of stacking model meta learner

Problem: I would like to improve accuracy of stock price prediction image classification model using candlestick charts. Base model: VGG16 and EfficientNet. Base model input: Two models independently ...
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Problem with dimension of multiclass classification

I'm building a MLP and after executing I obtained a 87.35 accuracy value, with is really good. However, when painting the confussion matrix and the classification report i see the accuracy is just for ...
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High loss value in Multi-class classification with MLP

I am building a Multi-class classification MLP and when i execute the code the loss value is really high. What do i need to change? I am using the UNSW-NB15 dataset. After encoding the categorical ...
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Accuracy not Increasing in fake news classifier

I have been trying to build a fake news classifier DL model. Notebook here Problem is when i put the same data in a Multinomial model it gives good accuracy. But ...
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What does that mean if the loss looks like this?

I have a problem. I have trained a model. And as you can see, there is a zigzag in the loss. In addition, the validation loss is increasing. What does this mean if you only look at the training curve? ...
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Maximize accuracy with Differential Evolution with R

I am trying to tune gradient boosting (caret package) with differential evolution (DEOptim) in R language. I have a question, is correct to define the maximum of accuracy at each iteration in my eval ...
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How to Improve MLP ANN accuracy

I am trying to improve the accuracy of my model over the UCI Breast Cancer Dataset. There's 426 records, and it is a binary classification model. ...
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Is it possible to train a Support Vector Machine to a specific accuracy?

From my understanding, support vector machines run on the premise of minimizing some error function, usually with the goal of maximizing accuracy overall. However, there are a lot of contexts, ...
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Can we train of a binary classifier with "A" to classify "a"?

I have a maybe naive question about the appropriateness of using binary classifications. This is a hypothetical example, so forgive me if it is too coarse. Let's say I want to train a support vector ...
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Determining 'Addictive' or 'multiplicative' seasonality and its forecast accuracy

Let's say that I have a "train" and "test" set data, how do I determine if my train set follows "additive" or "multiplicative" seasonality? Do I fit just the ...
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Which metrics that can be use to know "overfitting" model"?

Hello everyone i'm new to data science world. So i want to know if my model is overfitting. Usually i'm comparing training accuracy and testing accuracy. But on some reference many people using ...
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Same code vastly different accuracies

I am working on a node classification model, My friend implemented a simple 2 layer GCN and got an accuracy of 62%, I implemented the same code and got an accuracy of 50% we are both working on google ...
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Very low accuracy 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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How can I increase the correct predictions of one class using Weka?

I need to predict events (true or false) from a dataset, it has True and False samples. I'm just trying to predict as many "trues" as I can. Missing out some is no problem at all. The ...
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Kernel ridge regression (KRR), accuracy scale?

What does a good range for the accuracy score look like for the KRR model? For example, RMSE produces a value between 0 and 1, where values closer to 0 represent better fitting models. What's the ...
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Which model to use or how to preprocess the multi-dimensional data to classify?

I've a dataset containing only numpy arrays, without description on features. About 2k rows and 0.7k features. Divided into 1.4k train and 0.6k test. Applied baseline SVM and get F1 score of around 0....
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Overall acurracy +/- E (with 90% C.I.)

I am assessing the accuracy of my classification model. I performed a 4-folds cross-validation and I obtained the following OA = (0.910, 0.920, 0.880, 0.910). So, the average OA is 0.905. My dataset ...
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Top N accuracy for an imbalanced multiclass classification problem

I have a multi-class classification problem that is imbalanced. The task is about animal classification. Since it's imbalanced, I am using macro-F1 metric and the current result that I have is: ...
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When is it safe to say that improvements on a model are superfluous (even if one had e.g. "only" 80% accuracy)?

When is it safe to say that improvements on a model are superfluous (even if one had e.g. "only" 80% accuracy)? If the 80% accurate model is used for e.g. medical diagnosis or drug discovery,...
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Cross validation on train set or entire dataset

I have used train test split to split the entire dataset into train 80% and test 20%. Then i have used the cross val score with 5 folds on the X_train and y_train and got the max accuracy of 99.76 and ...
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How is the accuracy at the beginning of an epoch higher than that at the end of the previous one?

Below is a toy example of a CNN that I am trying out. As is observed, the accuracy at the beginning of the first epoch is at 84% and it increases to 96% by the end. With my understanding of ...
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Fluctuating accuracy for Naive Bayes Classifier and SVM

I am comparing the classification accuracy between Naive Bayes (NBC), SVM and a Neural Network. I am using a Dataset of ~18K and 26 Labels. In the current state the Neural Network get always an ...
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training loss decreases but training accuracy is also decreasing with epochs

I am working on the classification problem where by I am having a hinge loss function + other loss terms to optimize for which the input is the output from tanh layer at the end. But I can't reveal ...
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Spot Logistic Regression Training Error

My friend gave me this puzzle awhile ago and I've never figured it out. ...
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Improve true negative of a model

Apart from class balancing (oversampling, undersampling, SMOTE) and hyperparameter tuning, what are other methods that can be used to enhance a True Negative of a model for unbalanced binary target ...
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Balancing between accuracy and model overfitting

I have a dataset and I have built an XGBClassifier model from it. Without hyperparameter tuning, the model performs fairly well in training but on test which have some signs of overfitting (train ...
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What's the difference between micro-averaged precision and accuracy score?

I'm using sklearn's metrics module to try and evaluate a k-NN model's performance on the provided iris dataset from the ...
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Error from XGBoost missing data handling

I have a regression problem with a very large dataset >50 million rows, 81 features and 1 target, all positive float values unevenly distributed between 0 - 1 million. I've trained an XGBoost model ...
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Are these ANN training graph and validation graphs incorrect?

I have trained an ANN using Keras (Python3). However, I do not understand the training and validation loss graph. There's a big difference between the first and second training point. Is the graph of ...
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When to prioritize accuracy over precision?

I am working on a simple SVM project for the prediction of hepatitis c. I got my dataset from kaggle. When dealing with null values, I tried two ways, firstly by dropping data with null values, second ...
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Why do I get an almost perfect fit as well as bias variance tradeoff with my time series forecast?

In order to achieve scalable and robust time series forecast models, I am currently experimenting with metalearner ensembles. Note, that I am also using a global modeling approach, so all time series ...
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How do I test one-shot model preformance against flawed categories?

I'm in the process of reworking the ASAM database. Excerpted, it looks like this: ...
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Orange v3.32: Accuracy and precision not showing up

As explained in the orangehelp files the test and score widget would provide an accuracy colum like "CA". I only have MSE RMSE MAE and R2 besides the times. Furthermore, the predictions ...
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How to add class labels to confusion matrix of multi class classification

How do I add class labels to the confusion matrix? The label display number in the label not the actual value of the label Eg. labels = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P',...
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How to verify if the behavior of CNN model is correct?

I am exploring using CNNs for multi-class classification. My model details are: and the training/testing accuracy/loss: As you can see from the image, the accuracy jumped from 0.08 to 0.39 to 0.77 ...
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What is mean accuracy and why is it a harsh metric for multi-label validation?

The score method docs for scikit-learn's SGDClassifier have the following description: Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset ...
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Is it possible for the (Cross Entropy) test loss to increase for a few epochs while the test accuracy also increases?

I came across the question stated in the title: When training a model with the cross-entropy loss function, is it possible for the test loss to increase for a few epochs while the test accuracy also ...
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Deep Learning accuracy vs Confusion Matrix accuracy

I am working on deep learning with fer2013 dataset. After training the model I got val_precision: 0.9168 (precision: 0.8492) ...
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How much ground truth is needed for a classification model?

I have unstructured problem text that needs to be classified into categories(Multinomial classification). Depending on the component, which is a structured element that allows me to segment the data, ...
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What are the disadvantages of accuracy?

I have been reading about evaluating a model with accuracy only and I have found some disadvantages. Among them, I read that it equates all errors. How could this problem be solved? Maybe assigning ...
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What does it mean if the validation accuracy is equal to the testing accuracy?

I am training a CNN model for my specific problem. I have divided the dataset into 70% training set, 20% validation set, and 10% test set. The validation accuracy achieved was 95% and the test ...
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output F1-score instead of Accuracy

I have the code below outputting the accuracy. How can I output the F1-score instead? ...
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Why my models have a pretty high accuracy with a small training dataset?

I was wondering why my models (decision tree, svm, random forest) behave like that, with "high" accuracy on a small training dataset. Is it a sign of overfitting? The graph represents the ...
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model.fit vs model.evaluate gives different results?

The following is a small snippet of the code, but I'm trying to understand the results of model.fit with train and test dataset vs the model.evaluate results. I'm not sure if they do not match up or ...
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How to improve the accuracy of support vector machine algorithms in machine learning?

I am working with a machine learning project named "Diabetes prediction using support vector machine". In this project I have used Pima Indians Diabetes Database. Using SVM I have got 78% ...
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Random forest accuracy

I saw this video and I understood that to build a random forest are used different decision tree, with a different structure. My code about that is: ...
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how to improve recall by retraining a model on its feedback

I am creating a supervised model using sensitive and scarce data. For the sake of discussion, I've simiplified the problem statement by assuming that I'm creating a model for identifying dogs. Let's ...
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Does thereshold of classifier close to 0 make sense?

I have roc curve with AUC of 0.91. I applied the following function to determine the best threshold: ...
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Explain MAAPE (Mean Arctangent Absolute Percentage Error) in simple terms (intermittent demand forecasting)

n order to measure the accuracy of highly intermitted demand time series, I recently discovered a new accuracy measure, that overcomes the problem of zero values and values close to zero, when ...
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How to interpret a Regression Error Characteristic curve

How can I interpret the REC (Regression Error Characteristic) curve ? What is error tolerance and what is the area over the curve? What should be the accepted value for the error tolerance? How to ...

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