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.

Filter by
Sorted by
Tagged with
0
votes
0answers
27 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 ...
1
vote
0answers
9 views

CIFAR100 CNN does not learn

I am trying to train different CNN architectures on CIFAR100 and compare results with CIFAR10, with the same architecture (adjusting the predictive layer to manage the difference in the number of ...
0
votes
1answer
15 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 ...
1
vote
2answers
35 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 ...
0
votes
1answer
33 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 ...
1
vote
0answers
11 views

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 ...
1
vote
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, ...
0
votes
1answer
25 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?
0
votes
1answer
30 views

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 ...
3
votes
2answers
3k views

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 ...
1
vote
3answers
43 views

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....
1
vote
1answer
19 views

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 ...
0
votes
1answer
14 views

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 ...
1
vote
1answer
19 views

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 ...
0
votes
1answer
17 views

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 ...
1
vote
0answers
23 views

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 ...
1
vote
2answers
44 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 ...
1
vote
1answer
17 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 ...
0
votes
1answer
16 views
1
vote
1answer
16 views

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 ...
0
votes
3answers
62 views

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 ...
2
votes
1answer
26 views

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 ...
1
vote
1answer
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 ...
0
votes
1answer
20 views

Confusion matrix and accuracy not in sync

I am getting the following result for the confusion matrix and accuracy for a logistic regression model. ...
1
vote
1answer
21 views

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 ...
1
vote
0answers
29 views

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 ...
1
vote
0answers
21 views

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 ...
0
votes
1answer
52 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 ...
0
votes
0answers
28 views

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 ...
0
votes
1answer
14 views

Evaluation Metrics for Multiclass

How to obtain the Accuracy, Detection_Rate, False_Positive_Rate and ...
1
vote
1answer
31 views

How to obtain Accuracy of Feature Selection methods?

I used the following methods: Variance_Threshold: selecto_vth = VarianceThreshold(threshold=1.0) ANOVA: ...
0
votes
1answer
44 views

How to increase the Accuracy after Oversampling?

The Accuracy before ovesampling : On Training : 98,54% On Testing : 98,21% The Accuracy <...
2
votes
1answer
169 views

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% ...
0
votes
0answers
18 views

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 ...
0
votes
0answers
39 views

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 ...
0
votes
1answer
87 views

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. ...
3
votes
1answer
278 views

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 ...
0
votes
1answer
91 views

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 ...
1
vote
1answer
89 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 ...
0
votes
2answers
104 views

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 ...
1
vote
0answers
13 views

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 ...
1
vote
1answer
24 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 ...
0
votes
0answers
8 views

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 ...
0
votes
0answers
13 views

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 ...
0
votes
0answers
42 views

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 ...
0
votes
1answer
21 views

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 ...
0
votes
1answer
44 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 ...
0
votes
1answer
25 views

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
1
vote
1answer
54 views

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 ...
0
votes
0answers
9 views

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 ...

1
2 3 4 5
7