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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Drop in Accuracy and Precision of a Classifier on Validation set?

We have seen a significant drop in Accuracy by 20% and in Precision by 45%, after running Classifier on the validation set. During training, we had used 5cv and throughout our tests, we haven't seen ...
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Why does my CNN program improve neither the training accuracy nor the validation accuracy despite the error function drastically decreasing?

I have written a Python code to model a convolutional neural network (pastebin link) from the most basic Python libraries (numpy and math in addition to sklearn and pandas being used only for reading ...
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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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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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How to calculate a single accuracy for a model with multiple outputs in Keras?

Consider the following, rather simple, model: ...
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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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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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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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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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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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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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val_sparse_categorical_accuracy

I know the metric sparse_categorical_accuracy ...
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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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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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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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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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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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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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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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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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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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Machine learning accuracy for not a class-imbalanced problem

I would like know if the accuracy has an impact on not class-imbalanced dataset ? I know that accuracy is sensitive to class-imbalance and also always good to be able to appreciate precision and ...
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My data is highly overlapping, but when I apply logistic regression, it is giving an impressive accuracy of 79%. Why?

Logistic regression is supposed to work well only on data that is linearly separable. As we can see in the pair plot, the data points heavily overlap. The logistic regression model is in fact showing ...
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100% Accuracy and 0 loss in image classification

I am working on image classification using CNNs and the pretrained model VGG16, my dataset has 3 classes with almost 900 images per class. after traning for 5 epochs my model reached 1 accuracy with 0....
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How to State The Confidence of Accuracy/Inaccuracy?

Consider that I have a dataset automatically acquired by a machine that returns the following measurements: [111, 121, 114, 154, 149, 150] I then go and manually ...
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why the accuracy result and the loss result of an ANN model is inconsistent?

I trained a model based on an ANN and the accuracy is 94.65% almost every time while the loss result is 12.06%. Now my question is shouldn't the loss of the model be (100-94 = 6%) or near it? Why it ...
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Why would the accuracy of a model change when the loss doesn't?

I've trained 8 models based on the same architecture (convolutional neural network), and each uses a different data augmentation method. The accuracy of the models fluctuates greatly while the loss ...
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Test data accuracy from real world have lowest accuracy than validation data collected in simulation environment

Background: Problem type: Multi class classification The dataset contains around 1,000 samples (simulated dataset of sensor signals), where each sample is 2D i.e (1000 * 1000 * 8). Additionally, I ...
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Model Performance is Fluctuating

I am training a 3D u-net model for 3D medical images. My training data has 800 images, the validation data has 200, and test data has 200 images. when I try to fit the model, there is a fluctuation in ...
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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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21 views

How to measure multi-label multi-class accuracy

I have a model that has multi-label multi-class targets Example Age Height Weight Mark Distance Red Yellow Green Blue Black White 14 160 62 78 103 0 1 1 1 1 0 56 177 90 99 363 1 1 0 0 0 0 32 179 ...
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How to compute performance of a detection-classification system?

I use a yolo (y) to detect only one object and a multiclassifier (mc) that classifies that object. Now, the problem is: what I have to do with yolo's false positive and false negative, if I want to ...
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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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25 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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1answer
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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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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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