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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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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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?
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1answer
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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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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....
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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
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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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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 ...
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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 ...
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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
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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.
...
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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 ...
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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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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 ...
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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 ...
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1answer
14 views
Evaluation Metrics for Multiclass
How to obtain the Accuracy, Detection_Rate, False_Positive_Rate and ...
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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: ...
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1answer
44 views
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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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% ...
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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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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 ...
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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.
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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
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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 ...
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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 ...
