Questions tagged [accuracy]
The accuracy tag has no usage guidance.
228
questions
0
votes
1answer
22 views
accuracy, val_accuracy remains the same while training
I build a CNN based on the Chest X-Ray Images (Pneumonia) dataset and for some reason when I train the model I get the same accuracy and val_accuracy over epochs.
...
0
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1answer
20 views
How to derive false positive and false negative from top-k accuracy?
I am working on the following "equality identification" problem and become quite confused on how to reasonably define false positive and false negative in my case.
Problem:
Suppose I have a ...
-1
votes
1answer
9 views
training accuracy greater than validation accuracy
The problem that I'm facing is that the training accuracy of my model is way higher than the validation accuracy, were talking about an approximate value of 0.2. ...
-1
votes
0answers
20 views
My error seems too low for a model built for the Driven Data MDG Challenge, what can I do to troubleshoot?
It would help in understanding my problem if you've worked on DrivenData's MDG challenge (https://www.drivendata.org/competitions/1/united-nations-millennium-development-goals/).
I've built a model by ...
1
vote
1answer
19 views
Overfitting results with Random Forest Regression
I have one image that contains for each pixel 4 different values.
I have used RF in order to see if I can predict the 4th value based on the other 3 values of each pixel. for that I have used python ...
0
votes
1answer
32 views
XG Boost result interpretation for unbalanced datasets (Accuracy & AUCROC)
My dataset is of shape – 5621*8 (binary classification)
Label/target : Success (4324, 77 %) & Not success (1297, 23 %)
(success and Not success were been ...
1
vote
2answers
24 views
how can i compare two classification algorithms?
all,
i have two classifiers (xgboost and light gradient boosting) to predict if yes cancer or not. when i use roc_auc as my scoring method i get xgboost as 0.75 and light gradient boosting as 0.76. ...
0
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0answers
21 views
CNN Training accuracy shown during model.fit is not matching the predictions obtained on train data using model.predict
I'm using RESNET50 to classify images into 3 classes. The distribution of the classes is:
Class_0 : 43%
Class_1 : 32%
Class_2 : 25%
The training accuracy shown during model building process is ~80%, ...
1
vote
1answer
20 views
what are the next step after ML prediction and how to proceed?
I have trained an ML model with a good accuracy but what next?
I am facing difficulty in answering this question, how will you present your model.
Which framework do you use
How do you make sure ...
0
votes
1answer
25 views
Validation accuracy greater than training accuracy in cnn
I've splitted my training set in the ratio 80:20 and have developed cnn model with a dropout of 0.5. I'm getting an accuracy of 98%. But the validation accuracy stays greater than training accuracy. ...
1
vote
1answer
29 views
Info obtained from a confusion matrix
I am new to data science and I am trying to understand the use/importance of accuracy, precision, recall, sensitivity and f1-score when I have a confusion matrix.
I know how to compute all of them ...
0
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0answers
14 views
Definitive Values in Confusion Matrix
I built a convolutional LSTM model for the classification of 4-image time series. I used n keras ConvLSTM layers, followed by a time-distributed flatten and a few dense layers, finalized by a dense ...
0
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0answers
16 views
H2o Model Classification - Confusion Matrix Vs AUC
I am running a Classification Model in Python using H2o
When I checked the model Performance on test dataset I got
...
1
vote
1answer
37 views
Training accuracy is ~97% but validation accuracy is stuck at ~40%. What does it imply? [duplicate]
Training accuracy is ~97% but validation accuracy is stuck at ~40%.
I can not understand the meaning of two concepts and their relationship.
0
votes
0answers
11 views
loss increases but accuracy and macroF1 are still stable and don't change dramatically
I have a classification task with 2 classes. The dataset is imbalanced. When I train the model, at some point, the loss of test dataset starts to increase but the values of accuracy and macro-f1 don't ...
0
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0answers
14 views
CNN Architecture comparison standards
I want to add comparison of accuracy section in my study report on CNN Architecture for a medical data. I have already added the comparison by VGG 16, AlexNet etc. Is it a standard to compare the ...
2
votes
4answers
62 views
Evaluating Model Accuracy on a testing data set for a DecisionTreeReegressor Model
I am trying an exercise where I have been asked to "Evaluate each model accuracy on testing data set for a max_depth parameter value changing from 2 to 5".
The model here is DecisionTreeRegressor. I ...
1
vote
1answer
25 views
Is it conscientious to use a threshold for a model output in order to play on the recall and precision?
I have just finished reading an article about the F1 score, recall and precision. Everything was clear except the fact that the author, in his example (see https://towardsdatascience.com/beyond-...
0
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2answers
39 views
Tensorflow keras fit - accuracy and loss both increasing drastically
ubuntu - 20.04
tensorflow 2.2
dataset used = MNIST
I am testing tensorflow and i notice that validation sparse_categorical_accuracy (accuracy) and validation <...
0
votes
1answer
27 views
How to fix “Expected sequence or array-like”
I am trying to get the accuracy of the model and I am getting this error
TypeError: Expected sequence or array-like, got
Here's my code sample.
...
1
vote
2answers
39 views
Decision Tree gives 100% accuracy - what am I doing wrong?
My assumption is that my training set includes the test set, but I don't know how to change this.
...
-1
votes
1answer
20 views
How to increase a low recall value?
I am dealing with a HR Attrition Dataset which is highly unbalanced. I used Balancing technique like SMOTE to generate synthetic data and then used Gaussian Naive Bayes to Classify the Attrition. ...
3
votes
1answer
75 views
Balanced Accuracy vs. F1 Score
I've read plenty of online posts with clear explanations about the difference between accuracy and F1 score in a binary classification context. However, when I came across the concept of balanced ...
1
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1answer
26 views
Why is my training accuracy 0.0?
The Sizes of both the true label and predicted label are same still, the training accuracy is 0.0
...
0
votes
0answers
3 views
Brightness Adjustment During Pre-Processing and Model Accuracy
I have image datasets that consists of multiple level of brightness. Usually darker are more than the bright. All these images are collected from different places. Some of the images too dark that to ...
0
votes
1answer
23 views
How to compare performance between SVM and Keras models
I applied both SVM and CNN (using Keras) on a dataset. Now, I want to compare the performance of both models.
Keras model.evaluate function predicts the output for the given input and then computes ...
0
votes
1answer
39 views
I am getting very minimal mse values and not sure if it is correct?
Below is the linear regression model I fitted and not sure if I am doing the right way as I am getting neat to 99% accuracy
Fitting Simple Linear Regression to the Training set
...
1
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0answers
11 views
g-mean for binary classification doesn't use sensitivity of each class?
scikit-learn's contrib package, imbalanced-learn, has a function, geometric_mean_score(), ...
1
vote
2answers
83 views
Why do decision trees have low accuracy?
It seems to be generally acknowledged that decision trees have low prediction accuracy. Is there a concise explanation for why they have low accuracy?
I've read this so much, I've accepted it to be ...
3
votes
2answers
108 views
scikit-learn RandomForestClassifier always hits 100% test accuracy
I have been playing with a toy problem to compare the performance and behavior of several scikit-learn classifiers.
Brief, I have one continuous variable X (which contains two samples of size N, each ...
0
votes
0answers
19 views
jumpy validation accuracy graph
helo,
i have a very weird problem.
my validation accuracy graph is very jumpy, and i dont know how to fix it
this is the graph:
this is a multi label problem. i calculate accuracy with 0.5 threshold
...
0
votes
1answer
19 views
2
votes
2answers
103 views
Is it better to have higher train accuracy with lower test accuracy or higher test accuracy with lower train accuracy?
The results from my RandomForest model with 5 max features are as follows:
84% train accuracy
76% test accuracy
The results with 10 max features:
...
0
votes
0answers
69 views
Since is not possible test all the possible combination, what is the correct procedure to follow on building Machine Learning?
Sorry, I'm a little confused and this is a general question. How can I be sure that the procedure that I am following is the correct one? Following the steps for building a machine learning model, we ...
0
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0answers
22 views
U-Net: Accuracy drops to zero - How do I prevent this from happening?
I have a problem, when training a U-Net. When starting the Training, the Accuracy increases and the loss steadily goes down. At epoch 40, in my example, the validation loss jumps to the maximum and ...
1
vote
0answers
21 views
Classification Model showing different accuracy for SAME data?
This is my first post here, so kindly pardon any commonplace errors.
So, i have been training an XGBoost multi-class model on Google Colab. I am using a balanced dataset, with 31000 rows, where each ...
1
vote
0answers
31 views
What is mAP in object detection?
I have been reading through this blog in order to find what mAP is .In the sub heading of AP, they give the example of 5 apple images and finding out the average precision.As far I understand false ...
0
votes
0answers
12 views
How to set anomaly threshold depending of predictive model accuracy
Say I have a variable with a standard deviation STD
I have a predictive model to predict variable. The model accuracy is 80%
An anomaly is raised if difference (predicted_value - actual value) > ...
4
votes
3answers
101 views
Is my model over fitting or not?
I have 50000 observations with 70% positive and 30% negative target variable. I'm getting accuracy of around 96-99% which seems unreal of course and I'm worried that my model is over-fitting which I ...
1
vote
1answer
46 views
Is there any way to calculate the true,false positives and negatives for a regression problem
I am trying to predict the glucose values of the patients for example values like 45,256,115 etc. based on some features. Currently I am calculating the accuracy in means of RMSE,MSE,R². Is there any ...
0
votes
1answer
54 views
Same validation accuracy, different train accuracy for two neural networks models
I'm performing emotion classification over FER2013 dataset. I'm trying to measure different models performance, and when I checked ImageDataGenerator with a model I had already used I came up with the ...
1
vote
2answers
216 views
is it ok to get 100% accuracy in random forest classifier algorithm?
while i was building the model to predict the performance of machine using the features like OEF,working time,performance/head etc...
I splitted the training data using
...
1
vote
1answer
25 views
Using standard deviation as a metric for model selection
I'm really getting stuck with overfitting and I'm trying all I can to reduce it.
I want't to write a metric to help score models in a cv loop. I'm using 10x5 folds and still getting out of sample ...
0
votes
0answers
14 views
Can a binary classification model have lower accuracy, but higher F1-score [duplicate]
I am just reviewing some results where someone compared results of two binary classification models and the results state that model M1 achieved higher accuracy ...
1
vote
1answer
22 views
Data augmentation in deep training
I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is a fire classification (fire or not, on video frames), ...
1
vote
2answers
20 views
Metrics for same signal
Is there a metric in tensorflow.keras.metrics that counts how many time the predicted output and the real output have the same signal?
For example, if the ...
0
votes
2answers
34 views
Manual way to draw accuracy/loss graphs
During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below:
...
1
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0answers
22 views
Advantage of Centralized training in deep learning over distributed training
I know distributed training is the ultimate solution for scalability and solving resource constraints and sometimes distributed training outperforms centralized training in terms of accuracy. But I'm ...
0
votes
2answers
23 views
Present results of the best or the last iteration on dev set?
Which is the correct way - presenting the results of the best or the last iteration on the dev set in a paper? In research papers I usually see only one value, is it the best iteration of all?
I'm ...
31
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8answers
7k views
What would I prefer - an over-fitted model or a less accurate model?
Let's say we have two models trained. And let's say we are looking for good accuracy.
The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted.
The second has an ...
