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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Does eval loss decreasing slower than train loss indicate overfitting?

I am training a binary classifier using an efficientnetv2 model with a 1M image dataset where I do a 60/20/20 split. Does this graph mean that the model is over-fitting? I can see that the train loss ...
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Is my model overfitting ? Training Acc :93 % test accuracy 82%

I am using LGBM model for binary classification. After hyper-parameter tuning I get Training accuracy 0.9340 Test accuracy 0.8213 can I say my model is overfitting?...
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Low classification accuracy

I want to do a multi class classification with 6 classes. Whole dataset has 12750 and 56 features samples, so every class has 2125 samples. Before prediction I reduces amount of outliers by ...
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Overfitting CNN model - any relation to input image size?

If my CNN model is over-fitting despite trying all possible hyper parameter tuning, does it mean I must decrease/increase my input image size in the Imagadatagenarator?
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my k-fold validation is giving a lot of 100% in the concatenated confusion matrix, is it because of overfitting?

The confusion matrix is a concatenated one from a 5-fold stratified cross-validation of my data set. I used rbf kernel for the svm classifier. Is it telling me the classifier is overfitting? Plus when ...
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How to test if a curve is well described by an ellipse?

I have a set of data points in 2D, and I am trying to come up with some sort of statistical to determine if the points fall along an ellipse. My idea so far is to fit an ellipse to the points, take ...
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How do I choose the right parameters for just plain old simple standarddeviation?

I am evaluating different models that do binary classifications and basically generate trade signals. They make a prediction of either buy or sell for the next day. I look at 10 different underlying ...
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What causes explosion in MSE when training?

I (probably) well overfitted/overtrained a model. But I was just curious as to what might cause this type of behaviour. I carried on training (Epoch 1/50 is not the first epoch of training this model)....
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Why my models have a pretty high accuracy with a small training dataset?

I was wondering why my models (decision tree, svm, random forest) behave like that, with "high" accuracy on a small training dataset. Is it a sign of overfitting? The graph represents the ...
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Assess overfitting - All model metrics or only specific metric?

I am working on a binary classification using random forest with 977 records with 77:23 class proportion I got the below performance in train and test data (AUC = 81) Train data Test data My metric ...
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Not perfect accuracy when overfitting

Given a dataset and a decision tree that can be as depth as it wants, if you train the tree with all the dataset and then you test it against the same dataset and you get an accuracy that is not 100%, ...
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How should I improve my CNN binary classification model from overfitting and underfitting [duplicate]

I am trying to do the cats & dogs classification problem, the problem is that my model is overfitting and I have tried all the techniques I know in order to solve but nothing is working such as ...
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1 answer
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Is data leakage giving me misleading results? Independent test set says no!

TLDR: I evaluated a classification model using 10-fold CV with data leakage in the training and test folds. The results were great. I then solved the data leakage and the results were garbage. I then ...
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Do monotonic constraints prevent an XGboost to capture non-linear relationships in the data?

I have trained an XGBoost model (for a binary classification problem) and I have tested two scenarios: Scenario 1 - No Monotonic Constrained applied In this case I get a Gini on the training sample of ...
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Do we need to define model everytime we need to train data in LSTM?

Suppose if I have two datasets where 1st dataset is AAPL stock price and 2nd dataset is GOOGL stock price. Now if I define the model as ...
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Model Overfitting in text classification how to solve?

This is my CNN model i am doing text classification on mental health social media data. the model is overfitting as validation loss is much greater than training loss. There are three columns(Text, ...
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Linear Regression model underfitting

here is the source code of the model and the csv file. Using the csv file I have to apply linear regression Algorithm on it using "Sales" and "Profit". Train the model in such a ...
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Overfitting? Is it ok, if I've met my desired threshold?

I've trained a lightgbm classification model, selected features, and tuned the hyperparameters all to obtain a model that appears to work well. When I've come to evaluate it on an out of bag selection ...
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Which method is more suitable? overfitting of traning data or low accuracy?

Recently, I tested two methods after embedding in my data, using Keras. Convolution after embedding Maxpooling after embedding The first method's loss and validation loss are like, The second ...
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3 votes
1 answer
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Request: Confirmation on my understanding of overfitting and regularization concepts

Overfitted models tend to have largely different (some very high, some comparatively low) coefficients/weights for different feature values. So, this means the model (when drawn as graph) will have ...
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1 vote
2 answers
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How to make a model suffer from underfitting

I would like to show an example of my model when it is overfitting, and when it is underfitting. Now overfitting is pretty straight forward, just train on small data, and the model will remember the ...
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Handling Overfitting(diverging val loss and train loss) in Conv-LSTM

This is related to an Educational Research Project I'm using to learn stuff so please be kind. I'm working on a video binary classification task. I have almost 1400 videos of both classes. I also ...
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What is the problem that causes overfitting in the code?

** ...
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Validation accuracy have an almost good accuracy but loss function is high too

Based on my project, I find a little problem there with the statement like this. I want to make model with neural networks for text dataset. Then I use Pad Sequence for my text and Array for the ...
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Preprocessing for the final model to be deployed

Typically for a ML workflow, we import the data (X and y), split the X and ...
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How can I reduce overfitting in CNN model for image classification, even after data augmentation?

its my first time posting here. I'm trying to build a CNN model that identifies fruits from a dataset of apples, bananas, mixed fruits, and oranges. So far, one of the things I have done to prevent ...
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2 answers
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Imbalanced Dataset: Train/test split before and after SMOTE

This question is similar but different from my previous one. I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target ...
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Is there any better approach then K folds and nested K folds?

I am trying to understand what problem is K-folds solving. It does not seem to be solving data leakage at all, as we are still testing on test data and then taking an average of all test folds and ...
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Does class weighting encourage overfitting when the true class distribution is imbalanced?

I am working on a classification problem in which ~90% of samples come from class 1 while ~10% of samples come from class 2. I have been using various techniques to combat the class imbalance while ...
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1 vote
1 answer
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Poor binary text classification results

I have a binary NLP classification task to identify text that talks about a target topic from millions of sentences. Between 5-10% of sentences are positive, the rest is negative. I have trained ...
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Good classification, poor separation with TSNE/UMAP

I have been working on a classification problem for which I have been able to achieve good results across various classification metrics. I have been careful to ensure that I am not leaking ...
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2 answers
40 views

GridSeachCV not performing well on ML models

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2 answers
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How can I get the type of fitting in this curve?

Does that overfitting ? How can I interpret the curve ?
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Overfitting reason in 2-stage model

I'm trying to build an entity matching model. There are 2 kinds of features - binary (0/1) and text features. Initially I made a deep learning model that uses character level embeddings of some of the ...
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How do I know if this model is overfitting?

This is my example R script for a decision tree: ...
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Automate detection of overfitting models based on autoML libraries

I'm trying to use machine learning to impute missing data in series using some auto-ML libraries in python (so far : dabl, FLAML, auto-sklearn and AutoKeras). I know the way to detect overfitting in a ...
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4 votes
1 answer
118 views

What can I do when my test and validation scores are good, but the submission is terrible?

This is a very broad question, I understand and I'm totally fine if someone believes it's not appropriate to do it. But it's killing me not to understand this... Here's the thing, I'm doing a machine ...
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selection of loss function to avoid overfitting by autoencoder in prediction a figure with a sharp rise

I have to select the loss function to avoid overfitting by autoencoder in prediction of this figure that has a sharp raise, I would like to find how to avoid overfitting by autoencoder in prediction a ...
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Can Online DQN model overfit?

I am new in the area of RL and currently trying to train an online DQN model. Can an online model overfit since its always learning? and how can I tell if that happens?
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Data augmentation within epochs vs across epochs

Usually in deep learning data augmentation is applied by creating a new augmented version of each training sample for each epoch. Therefore the amount of training samples for each epoch stays the same ...
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Model variance increases during training

I trained a regression model with lightgbm and the learning curve doesn't look good: The model variance increases during training, which shows a kind of overfitting. Now, I tried many ways to fix ...
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Possible fixes for an overfitting random forest regressor?

I'm fitting a random forest regressor on my dataset (do note its not a classifer but a regressor since the target is a continuous variable) through a grid search cross-validation in sklearn. The ...
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What do "Under fitting" and "Over fitting" really mean? They have never been clearly defined

I am always getting lost when dealing with these terms. Especially being asked questions about the relationship such as underfitting-high bias (low variance) or overfitting-high variance (low bias). ...
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Can there be scenarios where an overfitted model in machine learning cannot be generalized?

Is it always possible to generalize an overfitted model? I know there are ways to handle overfitting, but can there be scenarios where overfitting cannot be handled in machine learning?
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Rule of Thumb for number of observations required to train a model with n independent variables?

I am aware adding more features to a model leads to overfitting of a model. Is there a rule of thumb for minimum number of rows required to build a model with n features in order to build a ...
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1 answer
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Learning curves

I am working on a multiclass classification problem. I want to know whether my model is overfitting or underfitting. I am learning how to plot learning curves and have 4 doubts. 1.) Is the ordering of ...
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1 answer
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Perfect scores for multiclass classification

I am working on a multiclass classification problem with 3 (1, 2, 3) classes being perfectly distributed. (70 instances of each class resulting in (210, 8) dataframe). Now my data has all the 3 ...
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2 votes
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Building machine learning models whilst penalizing them for complexity

I come from a predictive modelling background, where it's common to use differential equations to model physical or chemical or biological processes. Commonly to avoid overfitting people use AIC and ...
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1 vote
1 answer
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Will repeatedly fine-tuning on new data cause overfitting?

I have a binary classification model which I have trained on a training set. On the validation set its accuracy is ~85%. I set up early stopping which ended training when validation loss increased. ...
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2 votes
1 answer
126 views

Is it possible to use a Neural Network to interpolate data?

I am completely new to Artificial intelligence and Neural Networks. I am currently working on a plasma physics simulation project which requires a very high resolution data set. We currently have the ...
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