Questions tagged [overfitting]
Use this tag for questions related to overfitting, which is a modeling error (especially sampling error) where instead of improving model fit statistics, replicable and informative relationships among variables reduce parsimony, and worsens explanatory and predictive validity.
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Train a neural network on tabular data - loss on validation data stagnates
I am training a neural network on tabular data. I have a training set and a validation set with 55000 and 20000 samples respectively. I have trained it with 200 epochs. I observe a steady decrease in ...
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Overfitting detection
I am confused what I should take into account while trying to detect overfitting of a model.
Let's say I have a classification problem with the main metric being ROC-AUC. I split the data into train ...
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Manual feature selection and hyperparameter tuning
For a very small dataset that I have, when I set the parameters with the help of gridsearch, the test and training results are not acceptable at all and have a huge difference. I have to manually ...
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Increasing sample in regression problem
There is a tabular dataset with 65 samples and 20 features. Is there a way to increase the number of samples? SMOTE didn't help enough...
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Increasing data via RBF neural networks
Is there a code that can increase the number of data in one dataset with the help of a RBF neural network? A special design should be created for the model? Is there no pre-made model? Is there ...
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Radial basis function for increasing data
I have a dataset with 20 features and 65 samples. The models performed poorly, so I used scipy.rbf for interpolation and added 300 additional samples to the dataset. The models' performance ...
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Test Error is extremely higher than Training error after gridsearch and crossvalidation
I'm currently working on a machine learning project. It's a supervised learning problem. My goal is to predict for given data of an animal(keeping,size,weight,...) ingredients(energy,vitamine etc..). ...
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diffusion model: can't overfit on single batch
I am training the diffusion model from diffusion policy, specifically their vision notebook, on a custom dataset. As always, I try to make a sanity check of the pipeline, by overfitting on a single ...
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Random forest regression model for stock price prediction output has a flat line in the predicted values during the initial values
I have a random forest regression model for predicting the close price for stock data. I am getting model accuracy as like this:
/n Best Parameters: {'max_depth': 10, 'min_samples_leaf': 2, '...
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How do I identify overffiting when using GridSchearCV?
For context, I'm using Scikit Learn's GridSearchCV to find the best Hyperparameters of a Decision Tree.
I believe I understand Train, Validation, and Test sets and overfitting concepts when applied ...
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Objective function in Bayesian Hyperparameter Tuning
I have a question that has been going around in my head for a while and I'd like to leverage the wisdom of the crowd for getting a few opinions on it.
Let me describe the Problem:
I have a relatively ...
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Overfitting - Imbalance Classification using Deep-feed forward network
I have an unbalanced dataset, so I used SMOTEENN on the training set to resample, after training DFF,i could see the model is overfitting, could someone help me solve this?
Thank You.
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Question about the limitations of regularization
I am training a neural network which is overfitting. Even when I increase the number of parameters, the test lost plateaus while the training loss keeps decreasing. Can regularization (like an L1 or ...
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When can we claim that the training converged?
I've been working for a while in a binary classification problem with different types of neural networks. In this particular case, I'm using an 3-layer MLP with hyperbolic tangent activation in input ...
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Are my CNN loss and performance curves valid, or are they showing under or overfitting?
Thanks in advance for any help offered.
I am using a Keras CNN to perform binary classification (credit card transactions fraud vs non-fraud). Below is my results for 100 epochs.
It feels odd that the ...
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Strange situation RF classification: perfect train, test predictions all in a single category
I am puzzled by an issue with a boolean classification task using RF on a large dimensional dataset (1680 obs x 110 dim) and moderate imbalance (431 vs 1249).
train/test partition is random (0.8,0.2)
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As a result of cross-validation, the difference between the ideal auc values of the train set and the test set
In the attached figure, the x-axis is the number features of s removed, and the y-axis is the average auc score over 10 CVs.
I want to choose the point with the highest score while avoiding ...
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In which cases would we not like to go to the global minimum?
I would like to know in which cases we do not want to reach the global minimum. As I understand it, this can lead to overfitting. But why is this happening? And how can I avoid this in a real task?
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Student model overfitting in multiclass classification with knowledge distillation
I'm working on an OCT multiclass classification task using knowledge distillation. My teacher model achieves a solid 97% accuracy and its loss curve demonstrates good stability and generalization. ...
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How to handle time series data in ANN?
I want to use ANN to forecast the next #games played in my mobile game. There are 39 features: 9 features that describe the player's state (level, amount of in game-currencies, etc.) and the last 30 ...
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why validation accuracy is stuck at 75%?
i am using tensorflow=2.15.0 and keras associated with it I have made a cnn network to identify a total of 2294 images into 10 different classes or, data is divided as 229 images are contained in each ...
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Time Series forecasting with SVR
I am trying to forecast my data by Support Vector Regressor,
Here is my code:
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Why my simple resnet model overfit?
I work on data classification. My train results are good 90%+ accuracy, but the test accuracy/loss is inconsistent. I don't succeed to get rid of the overfitting. The images are grouped, so to ...
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Why is it so common to focus only validation performance during hyper-parameter optimization
Assuming a standard train/validation/test split, the common practice is (a) to train multiple models with different hyper-parameter configurations on the training set, (b) to evaluate these models ...
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How to recognize if a model is overfitting?
I'm trying to develop a real-time YOLOv8 model for detecting falls in a home environment.
The dataset I used consists of approximately 1100 images labeled as "fall" and "nofall," ...
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Why is my genetic algorithm overfitting so much?
I'm only training on a fraction of the data each generation:
...
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VGG16 Transfer Learning for image binary classification - suspected overfitting
I'm using VGG16 for transfer learning on a binary image classification task about human posture. The sample totaled about 2,000 images, with about 900 and 1,000 images in each category, respectively. ...
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Is my model overfitting based on my accuracy/loss curves?
Do those results indicate that my model is overfitting?
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282
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Random Forest overfitting to unbalanced data set
I am working on an unbalanced classification problem. I have have 2000 points which are positive, and 6000 points as -ve (chosen randomly from 100k universe of -ve points universe). Although I have ~...
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Can't overfit Transformer Encoder
In the below code I am trying to train a very simple Transformer Encoder model to basically do nothing with its input. Giving some arbitrary input vector x, the aim of the model is then to output that ...
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How to reduce the overfitting in my CNN model?
I am new in this world want practice for create a convolutional neuronal network. A model convolutional for image classification. I want classificate women and men images.
Previous, I did a course by ...
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overfitting or not
Hello so i'm building a classification model i train my on various models and these are the metrices so i want to know if ther's an overfitting or not
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Training ResNet50 model for binary classification
I want to use ResNet50 model to perform binary classification on a dataset spectrogram dataset. In order to do that I had to make a couple of modifications to the model's architecture:
Modified the ...
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Using Embedding For Regularization
Is using embeddings for regularization a valid practice?
My reasoning for that is that encoding training/tests datasets into smaller vectors would allow a smaller network with fewer parameters and ...
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Xgboost model predicting extreme values for events and non-events | Overfitting
Extreme values are predicted by my trained xgboost classification model in BQML for both events (Y=1) and non-events (Y=0). For all event observations, the model calculates probability scores that ...
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Avoid overfitting to noise by a noise penalty approach instead of early stopping?
I came across this article on deep learning for computational MRI and found an interesting sentence "However, early stopping has to be performed to not overfit to the noisy measurements." ...
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Challenges in Predicting Molecule Activity
I want to share a concern I have. I want to obtain a machine learning model that can predict whether a molecule exhibits biological activity. For this purpose, I have a set of molecules that do ...
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How to compare test vs train model performance
When comparing the test vs train model performance to ensure no overfitting (e.g., using AUC ROC as an example), is it better to select the model with the largest test score, or the model with the ...
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The cost function gets stuck at 120 epochs
I did a neural network in c++ to recognize handwritten digits using the MNIST dataset without any neural network pre-existing libraries. My network has 784 inputs neuron (the pixel of the image), 100 ...
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model.evaluate gives low results?
i have an image dataset and there are 6300 images with 5 classes . The features extracted and dataset reduced to 256 features. This dataset gives good results(%99) when tested ANN with Backpropagation(...
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Evaluating overfitting in a logistic regression model
I have developed a logistic regression model for a classification problem and obtained an AUC (Area Under the Curve) score of approximately 0.9. The model was estimated by splitting the available data ...
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why my accuracy and recall become higher in testing than training. How to interpret
Why in hidden layer 2 and 3 in neural networks scratch, the accuracy and recall I got low, but in testing the accuracy and recall become higher. In hidden layer 4 it's get weird when sampling strategy ...
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MobileNet validation loss not decreasing over time
I am trying to train a MobileNetV2 on a custom dataset, to image Classification task.
Cardinality is 864 images, split in 70%/20%/10%, balanced between the 3 different classes.
Weights are pre-loaded ...
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Is it possible to overfit a simple single variable linear regression model?
I searched this question and the answer I got was about a general regression model, rather than a single variable, linear regression model. If you increase the number of variables, you could fit a ...
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Overfitting still exists using different techniques on voice classification
I have 986 voice signals which have been collected by our team. The data set includes 745 healthy and 150 unhealthy voice signals. I split the data into 70% training and 20% validation and 10% test (...
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How do I know If my regression model is underfitting?
How do we evaluate the performance of a regression model with a certain RMSE given that a domain knowledge performance metric is not present?
Maybe MAPE is one way of comparing the performance of my ...
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Overfitted model [duplicate]
A classic question with an unclear answer, is it better to have an overfitted model performing better on a Cross-Validation setting, or a non-overfitted model performing worse?
In this context, higher ...
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Is my model overfitted?
I am using a naive bayes classifier to classify 20 newsgroup dataset. My accuracy on the training set is 97 and on the testing set is 89. Is my model overfitted?
If it is what steps can I take to ...
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Difference between Validation Error on Learning Curve and Validation Error Calculation in Machine Learning Model
I am encountering a problem where the validation error I see on the learning curve of my machine learning model is different from the validation error I calculate using the mean squared error function....
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Is there any concern for a pretrained model to overfitting to a fine-tuning task that has overlapping pretraining and training data?
Let's say my language model is pretrained on a general text corpus, and I want to use it for some specific downstream task that has it's datasets also included in the general corpus, is there any ...
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