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Questions tagged [overfitting]

Modeling error (especially sampling error) instead of replicable and informative relationships among variables improves model fit statistics, but reduces parsimony, and worsens explanatory and predictive validity.

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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. ...
Pavithra K's user avatar
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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 ...
vermillion flycatcher's user avatar
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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 ...
leapofFaith's user avatar
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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 ...
luckylogic's user avatar
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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) ...
Antonello's user avatar
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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 ...
JAE's user avatar
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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?
7wafer7's user avatar
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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. ...
phreak's user avatar
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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 ...
Cohensius's user avatar
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1 answer
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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 ...
beschichtung346's user avatar
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Time Series forecasting with SVR

I am trying to forecast my data by Support Vector Regressor, Here is my code: ...
Hadis's user avatar
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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 ...
J. Doe's user avatar
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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 ...
Enk9456's user avatar
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147 views

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," ...
Melissa Proietti's user avatar
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49 views

Why is my genetic algorithm overfitting so much?

I'm only training on a fraction of the data each generation: ...
BigMistake's user avatar
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70 views

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. ...
MaxHo's user avatar
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Is my model overfitting based on my accuracy/loss curves?

Do those results indicate that my model is overfitting?
Begnnier's user avatar
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112 views

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 ~...
Gupta's user avatar
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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 ...
SeñorDavid's user avatar
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26 views

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 ...
cleanet's user avatar
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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
Bilel kort's user avatar
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1 answer
256 views

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 ...
leapofFaith's user avatar
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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 ...
Adenilson Arcanjo's user avatar
1 vote
2 answers
131 views

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 ...
Scott Grammilo's user avatar
1 vote
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17 views

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." ...
Shihao ZENG's user avatar
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Classification Threshold Optimization after GridSearchCV

In my machine learning problem I am using a CNN to classify images. Since my dataset is imbalanced I want to perform classification probability threshold tuning so I can find the optimal balance ...
Throwaway123's user avatar
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Test accuracy plateaus when increasing max_depth -> inf

I've built a Random Forest model that classifies into four categories based on around 10 input features. To test the accuracy, I performed 5-fold stratified cross validation using the ...
okjdlsksjdwi's user avatar
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I defined a subclass of torch.utils.data.IterableDataset, which line of the following loader could have caused the issue and why?

The current model is overfitting quickly, i.e., the training error is minimized in a few epochs while the validation error remains high. Suppose training data is sufficient, i.e. self.cohort is set ...
user153719's user avatar
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1 answer
24 views

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 ...
Yasser Hayek's user avatar
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1 answer
159 views

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 ...
thereandhere1's user avatar
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54 views

Learning curve - Why does the train learning curve is flat?

I implemented a model in which I use Random Forest as classifier and I wanted to plot the learning curves for both training and test sets to decide what to do next in order to improve my model. ...
Nima Yousefi's user avatar
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47 views

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 ...
kripi's user avatar
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1 answer
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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(...
ömer özcan's user avatar
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44 views

Prevent Overfitting in Transfer Learning with small data

I have built a feed forward neural network to predict heat pumps energy consumption. Now, i want to use this model as a domain for other heat pumps via transfer learning. I want to simulate the case ...
MBC_222's user avatar
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13 views

Large Language model for regression on urls

I am trying to fit a BERT model for a URL regression task. I have a URL as a feature and I have to predict a metric M for it. Keeping a learning rate like $10^{-5}$, the model is overfitting in about ...
guesta's user avatar
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3 views

Is a predictor with high i formation value bad? Is there other way to cross check it?

So, I am preparing a dataset for an ML algorithm, but I have run into a problem - the thing is that around 23 of 96 predictors have got IV more than 0.5 (the lowest is 1.7) and I am curious if it is ...
user151138's user avatar
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57 views

Need insights in how to reduce overfitting with MLPClassifier

I am new to data science. Please bear with me as I ask this long question. I am trying to do Speech Emotion Recognition with MLPCLassifier on RAVDESS and Crema datasets. I am predicting only three ...
tirednemo's user avatar
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1 answer
91 views

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 ...
Derrick's user avatar
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1 answer
82 views

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 ...
Azareel's user avatar
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10 views

LSTM, seq to classification, why training on balanced data set yields such a good result?

I am using LSTM to classify the origin of people's names. The input data is not balanced over target classes, so I used oversampling to balance it. Now, I defined a simple LSTM model as follows: <...
user2856069's user avatar
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24 views

Model returns near perfect PR-AUC score but other metrics seem fine. Is my model overfitting?

I am currently working on a very imbalanced dataset: 24 million transactions (rows of data) 30,000 fraudulent transactions (0.1% of total transactions) The dataset is split via Year, into three sets ...
Hai Nguyen's user avatar
1 vote
0 answers
75 views

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 ...
elbarto's user avatar
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1 vote
2 answers
181 views

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 ...
Dietzsche Nostoevsky's user avatar
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1 answer
81 views

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 (...
Zara Nz's user avatar
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1 answer
76 views

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 ...
Mehmet Deniz's user avatar
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1 answer
31 views

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 ...
simon's user avatar
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1 answer
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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 ...
Colin Antony's user avatar
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1 answer
100 views

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....
John Smith's user avatar
1 vote
1 answer
314 views

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 ...
Brian's user avatar
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1 vote
1 answer
353 views

Can feature engineering avoid overfitting?

Can feature engineering avoid overfitting? If yes, are there any relevant papers that state this?
stack offer's user avatar

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