Questions tagged [accuracy]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0
votes
0answers
11 views

LSTM Train accuracy decreases in training time during the epoch

I'm training an LSTM model for sentiment analysis on a text corpus. There's a thing that I believe is not normal because I never have seen it in training my models. At the start of the epoch, the ...
1
vote
1answer
33 views

How to interpret training and testing accuracy which are almost the same?

Note - I have read this post but still don't understand I have a Naive Bayes classifier, when I input my training data to test the accuracy, I get 63.05%. When I input my test data, the accuracy is 65....
0
votes
0answers
23 views

How to increase accuracy and decrease loss of my model

https://jovian.ai/casella0798/badmodel I created the model above to predict red wine quality. I have 6 classes, from 3 to 8. Dataset is unbalanced, with a lot of classes 5 and 6. My model performs ...
0
votes
0answers
26 views

Confusion regarding accuracy and individual class performance

Consider a three-class classification problem where avg_cm1 and avg_cm2 are two average confusion matrices across 3 folds from ...
1
vote
1answer
25 views

Test set larger than train set [closed]

There is a two class dataset with 1121 values in total, having 230 from same class and 891 from the other class. The training set is choosen as 230+230=460 from both classes and the test set as the ...
-1
votes
1answer
31 views

Is 50% accuracy on a 4 label multiclass classifier good?

As the title says, I have a classifier with 4 labels. I am having trouble getting much above 50% accuracy in Predicting labels. I made sure the data and test sets are made up of approximately 25% of ...
1
vote
0answers
14 views

How to calculate separate binary accuracy metric for each label in a multi-label classification in Keras?

I'm stuck with this: def multilabel_binary_acc(y_true, y_pred): return [K.equal(y_true[:,n], K.round(y_pred[:,n])) for n in range(y_pred.shape[1])] But it ...
1
vote
0answers
8 views

different scored probability distributions for a classification model using python vs Azure ML Studio

I have a classification model which I initially built in Azure ML studio and then created a similar model in python (its similar because the algorithms in python VS Azure are a bit different so they ...
1
vote
1answer
81 views

sklearn “balanced_accuracy_score” sample_weights not working

I wanted a metric where I could weigh each class as I wish while measuring "total accuracy". sklearn seems to have this with ...
-1
votes
1answer
34 views

Why are the Loss and Accuracy metrics not suitable?

I have read in a book where it was described that Loss and Accuracy are only conditionally suitable for a statement strength. Other metrics are used like Precision, Recall, ... . Does anyone know a ...
0
votes
2answers
36 views

Why there is Different accuracy for two same trained model?

Trained the same model twice with the same dataset, the same parameters (Epochs, Batch Size, Learning rate, etc..). But both trained model shows different train as well as test accuracy on the same ...
0
votes
1answer
13 views

Lower training accuracy than testing accuracy (MLP/Dropout)

I am working on a problem of multi-class classification by MLP. I have set dropout to each middle layer. Now I observe the training accuracy is around 10% less than ...
1
vote
1answer
45 views

Hyperparameter tunning for Random Forest- choose the best max depth

I'm trying to choose the best parameters for random forest model. For that goal I hae run my model in loop with only one parameter and each time I have changed the number for the parameter max depth. ...
0
votes
0answers
10 views

Classification model performance - metrics for getting number in each class correct?

I'm fairly new to predictive modelling, so apologies if this is a stupid question. I am working on a classification problem (predicting if customers commit fraud or not), and have been comparing a few ...
1
vote
0answers
26 views

Tensorboard reported accuracy does not match my own calculations

I'm using Google's AI Platform to train an image classifier with Tensorflow (resnet-50 model). I have only two classes. Training set is something like 4K images in size and validation set 1K images. ...
0
votes
0answers
72 views

Reduce overfitting in a CNN model

We are Data science students and we are building a CNN model to pneumonia classification (dataset: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia ). We have applied a data augmentation ...
0
votes
2answers
40 views

Validation loss and validation accuracy stay the same in NN model

I am trying to train a keras NN regression model for music emotion prediction from audio features. (I am a beginner in NN and I am doing this as study project.) I have 193 features for training/...
0
votes
0answers
4 views

IOU accounting for the difference of the damage degree in GT and prediction

I have a model of a skin disease condition. It takes a skin image and predicts areas affected by edema. Skin can be in one of four degrees, so each pixel is assigned a value. Healthy: 0, mild: 1, ...
1
vote
3answers
179 views

Too high performances on a classification problem

I have a .json file as dataset of the type: and I am working on a classification problem in which I have to predict 4 classes, which are rhe semantic. I have worked through the problem, and after ...
0
votes
0answers
11 views

Appropriate naive benchmark for class recall in binary classification for unbalanced dataset

I have an unbalanced dataset with 3969 rows of customer data. The labels are whether or not they subscribed for a loan (yes or no). There are 3618 no cases (91.2%) and 351 yes cases (8.8%). I am more ...
0
votes
0answers
14 views

How to interpret Tensorboard Values for ObjectDetection

I am training an object detection neural networt using tensorflow and the ssd mobilenetv3 model. I want to compare different training datasets. Which metric in tensorboard should I use for this? My ...
1
vote
1answer
28 views

Accuracy graph of binary classification by CNN [closed]

Why in binary classification of images with CNN the loss and accuracy graph are so unstable? I mean accuracy of validation test does not increase smoothly, it goes to 80%, then comes to 60%, then ...
0
votes
2answers
23 views

Impossible to increase model accuracy [closed]

I'm building binary classification models on my company's dataset. The problem I'm having is that I haven't been able to increase the accuracy of my models. I have trained, tuned, cross validated ...
0
votes
1answer
43 views

Machine Learning validation data returns 100% accuracy [closed]

I'm Testing a Machine Learning model with validation data returns that return 100% correct answers, is it overfitting or the model works extremely well, do I need to continue training on more data? I'...
0
votes
1answer
42 views

accuracy at a false positive rate of 1%

I need to calculate the accuracy but at a false positive rate of 1%. I am not sure if it is the normal accuracy that we can calculate with sklearn or I need a customized formula?
1
vote
1answer
78 views

What's the most effective way to measure the accuracy of my multi-class classification NN?

I'm relatively new to data science, and am confused about how to measure the accuracy of a multi-class neural network. The model I'm building is attempting to predict the outcome of a given event with ...
1
vote
1answer
32 views

how print f1-score with scikit´s accuracy_score or accuracy of confusion_matrix?

I would like to print the f1-score. I got confused about the wording f1-accuracy score and accuracy score. What is the difference of these 2 scikit-learn metrics and how can I print the f1-score out ...
0
votes
2answers
30 views

What is the value of AIC criterion if RSS is 0? [closed]

The AIC formula is : $AIC = 2k + n Log(RSS/n)$ So if RSS is equal to 0, it is undefined. How do I deal with this? What value should it take?
-1
votes
1answer
17 views
0
votes
0answers
15 views

Is it possible to guess a pretrained CNN accuracy beforehand?

I am given a dataset of 2D medical images. I am asked to extract image descriptors from the hidden layer of the neural network pre-trained on the ImageNet dataset. I consider to use two networks: ...
0
votes
1answer
28 views

Model accuracy: how to determine it?

I have some doubts regarding the approach to build a classifier such as Multinomial Naive Bayes or SVM. I will go through the steps to see if the approach is fine. I have not a lot of experience in ...
1
vote
1answer
35 views

How are precision and recall better metrics than accuracy for classification in my example?

I'm trying to understand precision and recall with an intuitive example, but my calculation doesn't seem right. For example, there are 8 red balls and 2 blue ones. I'm stupid and just predict all of ...
0
votes
0answers
12 views

New testing data poor performance

Why my performance of new testing data is poor in terms of accuracy? I have created a CNN model for the classification. Before submitting into to model, I have normalized the data and split it into ...
1
vote
0answers
18 views

Why does my model fail to predict on the whole dataset?

So I have about 3000 images with 6 classes and this is what I did: 1 - split into training set and test set prior to anything with 20% test size 2 - performed data augmentation on the under ...
1
vote
0answers
38 views

Getting inf% accuracy for Random Forest model with scikit learn

I have pandas dataframe with 8000 observations and with many different columns -some of them are dates and hours that were converted into dummies (get_dummies) and some are numerical,and y label which ...
0
votes
0answers
38 views

Hyper tuning reduce the accuracy score, why?

I have performed hyper tuning grid CV search on KNN model. The actual accuracy score for my KNN was accuracy of 42.31 % without performing hyper tuning. However, after performing hyper tuning, the ...
0
votes
2answers
30 views

Hello, when i'm training my model with 80% data and testing with 20% data the accuracy is 49% and without split it's 99%

Hello, when i'm training my model with 80% data and testing with 20% data the accuracy is 49%. And when i'm training my data without splitting it's giving around 99%. I'm confused. Please help me with ...
1
vote
1answer
26 views

Drastic increase in accuracy while using pickle file with sklearn

I trained a xgboost classifier and it gave an accuracy of 49.99 % and i saved that model into a pickle file. When i ran the same data with pickle file (.pkl) it's giving an accuracy of 88.99 percent. ...
0
votes
1answer
25 views

Neural Network written in Kotlin working for simple math problems but not for MNIST classification

I'm building a neural network in Kotlin while reading the book "Neural Networks and Deep Learning" from Michael Nielsen. At the moment the network uses: sigmoid neurons, backpropagation, ...
0
votes
1answer
73 views

Why are the ANN training and validation accuracy graphs not smooth?

I am currently training an ANN using Keras (Python3), and I am slowly optimizing the model's architecture and came across something I have not seen before. The graph of the training and validation ...
0
votes
0answers
28 views

How to interpret a regression model performances (Loss, accuracy) under keras

I built a regression model using Keras. The following parms were used: ...
0
votes
1answer
37 views

How to increase model's test accuracy?

I am using the InceptionV3 model for training. Here is the link for the code (https://github.com/maxmelnick/tensorflow/blob/no_random/tensorflow/examples/image_retraining/retrain.py) Initially I have ...
1
vote
1answer
59 views

How to increase model's prediction accuracy

I am using the InceptionV3 model for training. Here is the link for the code (https://github.com/maxmelnick/tensorflow/blob/no_random/tensorflow/examples/image_retraining/retrain.py) Initially I have ...
3
votes
1answer
139 views

Making sense of loss and accuracy curves

This is an issue that I have come across over and over again. Loss (cross-entropy in this case) and accuracy plots that do not make sense. Here is an example: Here, I’m training a ReNet18 on CIFAR10. ...
1
vote
0answers
17 views

Improving Training accuracy of LSTM in Keras for ratings prediction given reviews

I'm new to Deep Learning and NLP. I found a dataset online which has reviews of different companies and their corresponding ratings from 1 to 5. I encoded the labels, then removed some basic stop ...
0
votes
1answer
50 views

Improving misclassification for one class in a multi-class classification task

Here I am trying to use 3 convolution layer neural network to classify a set of images (train data: (3249) , validation data: (487), test data: (326)) I have one class which is misclassified and I ...
1
vote
0answers
23 views

Why accuracy scores reported by Keras are low and erratic while the loss on the validation set is decreasing?

I'm trying to build CNN to predict two-label classification problem. Unfortuenetely, I can't share my model architecture, but I compiled the model using: ...
4
votes
2answers
359 views

Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
0
votes
0answers
44 views

ConvNet - What to improve regarding architecture, procedure and technique?

I have a dataset of 180k images of license plates (so, not necessary to localize the license plate at first) for which I try to recognize the characters on the images (License plate recognition). All ...
1
vote
2answers
89 views

Which model is better, one just before overfitting with higher accuracy or one with no overfitting and lower accuracy? [duplicate]

I am training a CNN model. In the first one I got a training accuracy of 87%(0.29 loss) and validation accuracy of 87%(0.30 loss) at 5th epoch, I kept training it for total of 15 epochs and as ...

1
2 3 4 5 6