Questions tagged [accuracy]
The accuracy tag has no usage guidance.
245
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14 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 ...
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2answers
20 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 ...
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1answer
23 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. ...
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1answer
13 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, ...
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1answer
22 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 ...
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19 views
How to interpret a regression model performances (Loss, accuracy) under keras
I built a regression model using Keras. The following parms were used:
...
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1answer
28 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 ...
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1answer
53 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 ...
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1answer
42 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. ...
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12 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 ...
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1answer
28 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 ...
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0answers
16 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:
...
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2answers
186 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 ...
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37 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
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2answers
38 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
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1answer
22 views
Uncertainty in connection to explainability
When I write "uncertainty" in this post I mean:
If I have a classifier into $a_1,..,a_n$ categories and for an observation $x$ I classify $x$ to $a_i$ with probability $p_i$, then the ...
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1answer
31 views
How do we know a neural network test accuracy is good enough when results vary with different runs?
In every paper I read about prediction models, the training accuracy and the test accuracy (sometimes also the validation accuracy) is stated as a discrete number. However, in experience, depending on ...
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1answer
37 views
is it wrong to use average='weighted' when having only 2 classes?
In the book 'Text Analytics with Python', the author provides model_evaluation_utils.py
In the code of the .py he does:
...
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1answer
13 views
how come accuracy_score recognizes the positive label and precision_score does not?
I am executing this code which works perfectly for me:
(I only have 'positive' and 'negative' sentiments):
...
4
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2answers
144 views
Accuracy is lower than f1-score for imbalanced data
For a binary classification, I have a dataset with 55% negative label and 45% positive labels.
The results of the classifier shows that the accuracy is lower than the f1-score.
Does that mean that the ...
0
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1answer
28 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.
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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 ...
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1answer
24 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. ...
2
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1answer
25 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
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1answer
33 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 ...
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2answers
25 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. ...
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23 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
29 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
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2answers
44 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
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1answer
31 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 ...
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0answers
18 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 ...
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31 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
...
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1answer
43 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.
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0answers
14 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 ...
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0answers
15 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
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4answers
73 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 ...
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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-...
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2answers
51 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 <...
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1answer
127 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.
...
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2answers
45 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.
...
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1answer
39 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. ...
4
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1answer
109 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 ...
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1answer
36 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
...
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0answers
4 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
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1answer
32 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
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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
...
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0answers
20 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
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2answers
121 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
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2answers
115 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 ...
