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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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How to aggregate classes for higher overall accuracy?

I have trained a classifier on a dataset that comprises a large number of classes. Some classes are easy to predict, whereas others are frequently misclassified. I would like to aggregate the classes ...
MuhammedYunus's user avatar
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Validation accuracy dip and recovery when restarting training

i was fine-tuning this large language model with Stochastic Gradient Descent and mid epoch i stopped training, and saved the model weights. Then at a later time, reloaded the weights and restarted the ...
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How are the mini-batches performance used to obtain the overall accuracy in ML packages?

For the sake of exemplification, let us consider the the time series convolutional neural network (CNN) classifier from the sktime (this question can be applied for ...
Rubem Pacelli's user avatar
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Loss increase while accuracy also increase [duplicate]

I'm training a fairly large classification model,and I'm having the below results. ...
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i need to improve accuracy of following code. it have 1 dataset folder having 7 folders. there are total 3076 images

importing libraries ...
raman deep's user avatar
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How to interpretmulti-class confusion matrix?

I'm looking at the SAMHSA Mental Health Client-Level dataset. I did some t-SNE plots (dropping irrelevant cols, normalizing some, one-hot encoding some) of 500k rows out of 6.5mil. I'm trying to do ...
Jackson Walters's user avatar
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Why is there a difference in Training Accuracy Output, when the training dataset is the same but the validation dataset is different?

I am looking at the output of a multi-class image segmentation deep learning model. I used U-Net to implement this. I am confused about why the training accuracies are different for a different ...
user10529827's user avatar
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Random Forest Classifier Removing Features using Top-N Features Method

I am a new-comer to data science and machine learning techniques and processes. I'm working on a personal project that predicts the winner of an NBA game using a random forest classifier. I have ...
Vishnu Vennelakanti's user avatar
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how to fix my increasing validation loss and decreasing training loss?

here is the code that got me this, please i need an advise on what to do to correct this. ...
Michael Oyeboade's user avatar
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The accuracy of my model (AutoEcoder) Stops always at 50%, I want 95%

Hello StackOverflow community, I'm working with a dataset comprising binary vectors. For each instance in my dataset, there is an input vector X and an output ...
Brm-Covißio's user avatar
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Is this the appropriate way to calculate a multiclass reliability diagram for model calibration?

I'm trying to generalize reliability diagrams [1] to a multiclass classifier and implement that using pytorch and pytorch-metrics. So far so good but I'm somewhat confused about the definition of ...
Nirro's user avatar
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Why is my genetic algorithm overfitting so much?

I'm only training on a fraction of the data each generation: ...
BigMistake's user avatar
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How to deal with categorical disalignment in test and train in binary classification problems

I have a train and test datasets (600k observations) that have different categories for the same categorical variable. For example train has the categorical variable Letters having unique categories ...
kyara's user avatar
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Finding Accuracy, Recall, Precision, and F1 from Matlab Confusion Matrix

I'm working on a project to find the highest accuracy between KNN and a Decision Tree for Classification using Matlab. How to calculate the Accuracy, Recall, Precision, and F1 from the output below? ...
willow's user avatar
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Why the test accuracy showing some odd behaviour in comparison to train accuracy?

I am currently training an ANN using Sequential(a class from Keras API within tensorflow), and I am optimizing the model's architecture and came across something I have not seen before. The graph of ...
Aach_copro's user avatar
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Is my model overfitting based on my accuracy/loss curves?

Do those results indicate that my model is overfitting?
Begnnier's user avatar
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How to infer the following graphs for dimensionality reduction?

I'm dealing with a high-dimensional(1600 features, 9500 columns) binary classification problem. My current accuracy, and other metrics are not upto the mark. I am trying different feature selection ...
Tanmay Sharma's user avatar
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How to calculate the training accuracy of a decision tree?

The hint given was to construct a confusion matrix.
Praveent Thamil Mani's user avatar
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Improving Normalizing Flows Accuracy

What are some techniques one might use to improve the accuracy of normalizing flows? I am training a flow in a high-dimensional space but it seems like there's always at least one or two dimensions in ...
vermillion flycatcher's user avatar
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warning 'newdata' had X row but variables found have Y rows

Linear Discriminant Analysis (LDA)+logistic regression model lda_model <- lda(train_labels ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = train_data) LDA scores for the training ...
Maisha Maliha's user avatar
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Validation Accuracy incorrect when multiple outputs are in the dense layer!

I have a set-up a parallel LSTM architecture with two LSTM layers and one Dense layer producing several outputs which are converted into a probability with a sigmoid function on the dense layer. For ...
Abdi's user avatar
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Is there a way to focus mainly on high precision when fitting a tree model?

I have a dataset with 95% false and 5% true labels, some 200000 samples overall, I'm fitting a LightGBM model. I mainly need to focus on high precision and have low number of false positives, I don't ...
Fireant's user avatar
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Is it correct to use t-test on accuracy values of two different classifiers?

I have two datasets (500 data points with 15 variables and a binary output): The first one includes all variables. The second one includes all variables, except one is removed. I want to check the ...
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Why did both my precision and recall (hence also F1) improve dramatically but the accuracy lowered?

I split all the audio recordings in my small dataset into short clips, thereby "created" more samples. All of precision, recall and F1 almost doubled (from around 0.35 to around 0.65), but ...
Hok Yan Pun's user avatar
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Is it possible to detect early if a model is bad?

Let's say we have a model and have just started to fit it, the first epoch out of many. The first epoch shows awful results. Does it make sense to continue training hoping the results will be better ...
Putnik's user avatar
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Decision making in a binary classification problem

Consider a two-dimensional feature space in which the line $\mathbf{w}.\mathbf{x} + b = 0$, where $ \mathbf{w},\mathbf{x} \in \mathbb{R}^ 2 $ and $b \in \mathbb{R}$, separates linearly separable data ...
Tirthankar's user avatar
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Recall and Precision ML models

I use decision trees for a binary classification. To evaluate the model, I use K-fold cross-validation, where k = 10. When I run the model n times, I get a relatively constant accuracy across all ...
Jan Jansen's user avatar
1 vote
1 answer
638 views

How to measure accuracy of GPT model

I am working on a model to build questions automatically from some text My model will analyse provided article and ask authors questions that can help improving their articles How can we measure the ...
asmgx's user avatar
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Which is the best binary classification model? Train and Test Accuracy are similar

I am building a binary classification model where classes are imbalanced but used SMOTE, I used 4 different models to compare performance and decide which to choose. They have same train and test ...
Sarah's user avatar
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What is the highest possible prediction accuracy when I flip some labels at random?

I want to predict MNIST labels in a binary setting using a simple MLP model (0 for digits 0-4 and 1 for 5-9). For the train and test data, I randomly flip 25% of the labels. Is the maximum achievable ...
Johannes97's user avatar
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1 answer
23 views

How to Data Engineer a dataset to get the best featurres to predict a target class?

In my dataset, I have data of IDs that don't create any meaningful relationship with each other and when I test that dataset on different models I am not getting accuracy more than 40%. Anyone can ...
Farhan Aslam's user avatar
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1 answer
589 views

Torchmetrics Binary Accuracy and Multiclass Accuracy don't match

in my program I have the problem that for a 2-class classification problem my multiclass accuracy and binary class accuracy don't match. I have generated a very small sample example where you can see ...
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Why is accuracy score suddenly becoming 1 on using XGBoost?

I am developing a music classification system based on a kaggle dataset: https://www.kaggle.com/datasets/vatsalmavani/spotify-dataset I tried using K means classifier to classify the songs into 4 ...
zero_day's user avatar
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How can I improve accuracy of my ensemble (or anywhere in the code where I can increase accuracy)?

I am pretty new to machine learning, so if my code is not good, please bear with me. ...
MrPizza FarmerDude's user avatar
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2 answers
969 views

KNN Accuracy training

After I developed my model using KNN I get the following accuracy: Train Accuracy :: 1 Test Accuracy :: 0.24 What is the accuracy of my model?
user150859's user avatar
1 vote
1 answer
156 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 no other ...
orde.r's user avatar
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which Keras accuracy metric for multiclass classification

I am training a CNN for multiclass image classification into 4 images , what accuracy metric should i use from Keras. My labels are not one hot encoded as I am trying to predict probability of ...
zero_day's user avatar
1 vote
1 answer
253 views

Testing accuracy is higher than training accuracy

My testing accuracy is way higher than my training accuracy. I have used feature selection and split the data into training, validation and test sets. ...
Akshita's user avatar
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1 vote
2 answers
193 views

Neural network not learning at all

I am training a MLP on a tabular dataset, the pendigits dataset. Problem is that training loss and accuracy are more or less stable, while validation and test loss and accuracy are completely constant....
CasellaJr's user avatar
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I am getting all scores as 100% on my machine learning models. Is it okay to have this kind of result?

I am getting all scores for my ML model as 100% for the Extra Trees Algorithm. I am applying the necessary pre-processing steps (duplication removal, correlations validating, cardinality validation, ...
Nathindu's user avatar
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how do I test if overfitting exists when I use cross_val_score method?

I got the following code form a book on xgboost. I wonder whether this is a correct way of analyzing cross validation score for overfitting purposes. mean accuracy is 81 which can be okay. but what if ...
Mehmet Deniz's user avatar
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1 answer
217 views

Why is the accuracy on train dataset not always 100% while we use the same dataset to train the model?

Though tree-based ML algorithms give us 100% accuracy on train dataset many times, but why is this not happening every time. I know this results in overfitting but why not 100% accuracy every time on ...
Mystical_soul's user avatar
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How to calculate accuracy of a logistic regression?

A logistic regression involves a linear combination of features to predict the log-odds of a binary, yes/no-style event. That log-odds can then be transformed to a probability. If $\hat L_i$ is the ...
Dave's user avatar
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Do I need to use always the same "Test" dataset to compare between different models?

I have two datasources A and B, and I want to check how several methods can affect the accuracy of my multi class models: If I use cross-validation with validate dataset to obtain the best hyper ...
Just_4n0th3r_Pr0gr4mm3r's user avatar
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1 answer
75 views

A curve val_loss and loss in keras after training a model

Can anyone help me, is my model overfitting or underfitting? I want to make sure the model is well done before starting to deploy Also, I use categorical cross-entropy loss I have asked before, but I ...
Manar-01's user avatar
2 votes
4 answers
4k views

99% accuracy in train and 96% in test is too much overfitting?

I have a binary classification problem, the classes are quite balanced (57%-43%), with a GridSearch with Random Forest Classifier I obtained the best hyperparameters and I applied the model to train ...
SimoneA's user avatar
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I get 100% on my test set using random forest. What is wrong?

I am getting 100% accuracy on my test set when trained using random forest. Is there something wrong with my model? Code: ...
hre0's user avatar
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3 votes
1 answer
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Accuracy vs Categorical Accuracy

I was running a DNN model that uses ResNet50 for Transfer Learning. While fitting the training data on my model to check the initial trend (would run for more epochs if initial trend seems right), I ...
Harsh Khare's user avatar
2 votes
1 answer
1k views

Validation and training loss of a model are not stable

Below I have a model trained and the loss of both the training dataset (blue) and validation dataset (orange) are shown. From my understanding, the ideal case is that both validation and training loss ...
Avv's user avatar
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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 ...
tikendraw's user avatar
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