Questions tagged [accuracy]
The accuracy tag has no usage guidance.
0
votes
0answers
15 views
The effect of removing pooling layers in the model's accuracy
I know that removing pooling layers will lead to an increase in dimensionality and subsequently, make the training to be more time-consuming. But I'm wondering if ...
0
votes
0answers
11 views
Looking at the graphs, what can I do to increase the accuracy of my neural network
I am trying to solve a regression problem using Keras. The data is time series based and I am using one LSTM layer and two Dense layers.
Following code shows the basic model setup
...
1
vote
1answer
43 views
How to know if a model is overfitting or underfitting by looking at graph
Just recently got my hands on tensorboard, but can you tell me what features should I look for in the graph (Accuracy and Validation Accuracy)
And please do enlighten me about the concept of ...
0
votes
0answers
26 views
Effect Size in comparison of overall accuracy from Random Forest
I would like to compare two overall accuracy statistics (of a Random Forest classifier).
My Data:
two samples with each containing 25 features and one categorical class variables (9 different ...
3
votes
4answers
132 views
What does it mean when my test data accuracy is higher than my training data?
I'm using four years of data, training on the first 3 and testing on the fourth. Using LSTM w/ Keras. My test data set (which has no overlap at all with the training) is consistently performing better ...
0
votes
0answers
19 views
MAPE as an accuracy measure
I want to run and compare time-series forecast methods.
Mean Absolute Squared Error (MAPE) is considered one of the strongest metrics for accuracy.
My question is the following:
If you do $1-MAPE$ ...
0
votes
0answers
8 views
Manual setting of target variable based on features' minimum values: f1 score = 1
I am building a classifier for user engagement in my website. Basically, since there are no "proxy" for engagement, i.e. there is no pre-defined target variable, I came up with minimum thresholds ...
0
votes
0answers
40 views
How to improve accuracy of a binary classification in neural network
I tried to program a binary classifier to predict whether a customer belongs to one class or another.
I have over 200k feature vectors, consisting of numeric 12 features each and assume that should ...
0
votes
0answers
53 views
Multiclass classification on imbalanced dataset : Accuracy or micro F1 or macro F1
I have a multiclass classification problem. Further, an instance can be assigned to exactly one class. My dataset is highly imbalanced. I know that accuracy is not a good metric to use in this case ...
1
vote
0answers
10 views
Different accuracy values using the same saved model in tensorflow
I have trained a model in Tensorflow (for some signal classification problem, using mostly convolutional layers, no RNNs), saved It using the callback checkpoints. When I'm testing the said model on a ...
1
vote
2answers
91 views
Loss value going down while accuracy remains constant?
While I am training, it seems like my loss is going down, but my accuracy remains constant throughout training. It always seems to go towards 0.0023 no matter how I tweak my network, input data length,...
0
votes
2answers
68 views
Smaller test data set than training data set in machine learning
I would like to train different machine learning algorithms (SVM, Random Forest, CNN etc.) for the same data set (e.g. MNIST) und then compare their accuracies.
The goal would be to find out from ...
1
vote
2answers
59 views
0
votes
0answers
16 views
Accuracy doesn't increase in Binary Classification with 3D coordinates as data
I have 4000 catalogues of galaxies, in each there are 34700 objects, for each of it I have x,y,z coordinates. I want to do a binary classification creating a model which should be able to determine ...
0
votes
1answer
36 views
Training accuracy decreases
I have a program in which I use sequence to sequence approach as a prediction model with attention. The problem is, while training, the accuracy is always decreasing at each epoch, like shown in the ...
0
votes
1answer
46 views
What could cause training CNN accuracy to drop after 7th epoch?
I am training a CNN on some new dataset.
Usually, the accuracy steadily improves over 10-20 epochs.
I have created a new but similar dataset (using same methods) but now I see a sharp drop after 7th ...
1
vote
1answer
44 views
Can `k=1` be a good choice for K neighbors classification?
Running sklearn.KNeighborsClassifier() on Kaggle's Leaf Classification sample (set of 99 species, 10 specimen each), with defaults kNN parameters and a grid search ...
3
votes
2answers
137 views
How to compare paired count data?
I am working with a machine learning approach that counts cars in images. I have a predicted dataset, which is the predicted output from the machine learning approach and a paired "true" dataset, ...
1
vote
2answers
478 views
loss/val_loss decrease but acc/val_acc are consistent
I don't know why I am getting such good results.
...
7
votes
3answers
460 views
Inverse Relationship Between Precision and Recall
I made some search to learn precision and recall and I saw some graphs represents inverse relationship between precision and recall and I started to think about it to clarify subject. I wonder the ...
1
vote
2answers
137 views
loss/val_loss are decreasing but accuracies are the same in LSTM!
I am trying to train a LSTM model, but the problem is that the loss and val_loss are decreasing from 12 and 5 to less than 0.01, but the training set ...
1
vote
2answers
47 views
Classifier performance evaluation
I have an unbalanced dataset which has 920 samples in total, 689 belong to the first class, and 222 to second class. and both classes are significant for me.
so when building a classifier model such ...
0
votes
1answer
53 views
CNN accuracy and loss doesn't change over epochs for sentiment analysis
I am performing text classification as Good [1] or Bad [0]. The texts are preprocessed and converted to Vectors using Google Word2Vec. Further CNN architecture is used for training. I have roughly ...
1
vote
1answer
24 views
Accuracy after selftraining didn't change
I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would ...
7
votes
2answers
710 views
Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?
I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting.
But I am confused that which ...
0
votes
1answer
30 views
Can this be a case of multi-class skewness?
I have been working on an email data set, and trying to predict the owner team for it. But my prediction accuracy is just 58%. I have implemented data cleansing, null value removals, duplicate removal,...
0
votes
1answer
118 views
Keras: extreme spike in loss during training
I am training an LSTM for time series forecasting and it has produced an extremly high loss value during one epoch:
...
2
votes
1answer
37 views
Why is my training accuracy decreasing higher degrees of polynomial features?
I am new to Machine Learning and started solving the Titanic Survivor problem on Kaggle.
While solving the problem using Logistic Regression I used various models having polynomial features with ...
1
vote
1answer
477 views
Best CNN architecture for binary classification of small images with a massive dataset [closed]
The title has it all...
Any tip is welcomed.
Should I use a very deep convolutional neural network ?
Use a large amount of filters ?
Parallel layers ?
Dataset examples:
1) "Good"
2) "Bad"
1
vote
1answer
39 views
How do data scientists test the claimed accuracy of a model?
For example, if you are a data scientist at a company, and a salesperson offers you a pre-trained model that is claimed to have a 90% accuracy of detecting fraudulent transactions. How would you go ...
1
vote
0answers
15 views
Different testing and training accuracy values within a NN TensorFlow structure
In order to select the optimum number of my gradient descent algorithm, I had used a for loop of 1500 iterations and each 100 iterations training and testing accuracies are printed. Here everything is ...
1
vote
0answers
24 views
Regression model Giving the same prediction for all new inputs until i load the model again
I have build a regression model that has some decent accuracy measures.
I have pickled it and loading it another project. However it is producing the same predictions every time when i pass new ...
1
vote
1answer
23 views
Autoencoder doesn't learn to reduce dimesions
I coded a neural network from scratch in Python. I tried it with the XOR problem and it learned correctly. So I tried to encode an Autoencoder with 3 inputs (and therefore with also 3 outputs) to ...
1
vote
1answer
18 views
My accuracy changes throughout every epoc but the val_acc at the end of each epoc stays the same
I am training a transfer learning CNN on 161 pictures that have been augmented into 966 photos, 4 classes. I am training on a balanced data set, so there are 52 images of each class and also in the ...
1
vote
0answers
12 views
Is it possible to have a better performance with C4.5 than Bagged tree?
I am not sure but I have read that bagged trees are used to improve the accuracy of a single tree methods such as C4.5 but applying both of them over the same dataset I got better accuracy with C4.5, ...
2
votes
1answer
14 views
How should multiclass classifier performance be measured when one type of error is preferred over another?
Sorry if this question has been asked before--I am having trouble searching this topic since I'm not sure of my wording.
Say you have a classification problem where there are more than two labels ...
2
votes
2answers
78 views
Accuracy differs between MATLAB and scikit-learn for a decision tree
Is there any possibility to vary the accuracy of same data set in matlab and jupyter notebook by using python code ?
For same data set, at first I applied it in matlab and get 96% accuracy for ...
1
vote
1answer
95 views
Metrics for presenting RNN/LSTM result
I am working on a two different architecture based on LSTM model to predict the users next action based on the previous actions. I am wondering, what is the best way to present the result? Is it okay ...
1
vote
1answer
108 views
Confidence Score For Trained Sentiment Analyser Model
I have trained a text based sentiment analysis model, using SciKit-learn and custom data. I have the model ready and it works fine in predicting a text to a class (Positive or Negative or Neutral). I ...
1
vote
2answers
94 views
How can I know if my NN TensorFlow model is overfitted or not?
I am new with TensorFlow (Python) and I can not juge my obtained results in terms of training and testing accuracy
I am using the GradientDescentOptimizer with a learning coeff equal to 10^(-4) and ...
1
vote
2answers
69 views
How can I check if a bigger training data set would improve my accuracy of my scikit classifier?
How can I check if a bigger training data set would improve my accuracy of my scikit classifier, is there a method or something?
1
vote
1answer
112 views
test accuracy of text classification is too less
I have a data set of movies and their subtitles.My task is to classify them based on their ratings-[R,NR,PG,PG-13,G].I have 13 examples for each class.
I preprocessed the subtitles in the following ...
2
votes
1answer
44 views
Performance of model in production varying greatly from train-test data
I was wondering if anyone has any advice on where to start digging for this problem. I have a model which has gone through development and all train/cv/test data sets now perform above 95% both for ...
0
votes
1answer
47 views
My training accuray is 1.0 but the predictions on the training data are wrong
My neural network is not working right, and I am trying to find out what is up.
I inserted just three images to a transfer learning (mobilenet) neural network. The three images' classes are:
array([[...
1
vote
0answers
20 views
Why is my model not learning and the accuracy varying wildly? How can I smooth out these issues?
I'm trying to predict from initial trade terms whether a trade will be rejected, accepted, or abandoned.
Here are the stats for the training set and the test set:
Training:
...
1
vote
1answer
29 views
How can we show that one model might have higher accuracy than another model but at the same time lower AUC?
Assume that we have two classification models M1 and M2 that are evaluated on five test instances. How to show with an example that M1 can have a higher accuracy than M2, while at the same time M2 has a ...
0
votes
2answers
123 views
Validation accuracy is always close to training accuracy
I am trying to tune the hyperparameters of a LSTM I have to do time series forecasting. I have noticed that my validation accuracy is always very close to my training accuracy. I am not sure whether ...
1
vote
0answers
71 views
How do I perform Leave One Out Cross Validation For Top n Recommendation Sytems?
I am new in making recommendation systems . I am using the surpriselib library to evaluate my recommendations. All the Accuracy Metrics are well supported in this library. But I also want to compute ...
2
votes
1answer
657 views
human level performance on ImageNet, top-1 or top-5?
Anyone have pointers to where the human level performance on ImageNet comes from?
I found a reference to 5.1% accuracy (top-1? or top-5?) from here.
0
votes
2answers
52 views
Deep Learning Network decreasing in accuracy
In order to familiarize myself with semantic segmentation and convolutional neural networks I am going through this tutorial by MathWorks:
Semantic Segmentation Using Deep Learning
I did not use the ...
