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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24 views

Why does the overfitting decreases if we choose K to be large in K-nearest neighbors?

I am studying machine learning and I am focusing on K-nearest neighbors . I have understood the algorithm, but I have still a doubt, which is on how to choose the K for the number of neighbors. I ...
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24 views

How we can identify the problem of Overfitting and underfitting and maintain bias?

Basically, I'm new to the data science field, and I'm getting a little bit of confusion about overfitting and underfitting. Are overfitting and underfitting is ...
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20 views

Regularization hyperparam tuning during training

I have an idea for a regularization-hyperparam selection method, which I haven't encountered before and can't find on Google, but I'm sure someone has already tried it and I'm wondering what are the ...
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SVM is taking too long for hyperparameter tuning

I am running SVM,Logistic Rregression and Random Forest on the credit card dataset. My training dataset has the shape (454491, 30). I performed 5-fold cross validation(which took more than an hour) ...
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1answer
26 views

How do you identify whether your RMSE score is good or not?

Im building a XGBoost regression model to predict the values in the range of -3 to 3. Im using Root Mean Squared Error to evaluate the model. With hyper-parameter tuning and everything the best scores ...
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28 views

Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented ...
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2answers
34 views

100% accuracy on both train and test after feature engineering

The original dataset is of ~17K compound structures almost equally divided with labels indicating yes or no, after heavy use of mol2vec and rdkit I have created ~300 datapoints Using the boosted trees ...
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1answer
25 views

How to identify Overfitting in RandomForestClassifier?

Im building a sentiment classification model using RandomForestClassifier. I got the training accuracy of 99.65 & cross-validation( RepeatedStratifiedKFold-5 folds) accuracy of 97.29. I used f1 ...
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18 views

IIoT Sound Classification with Little Data

[Problem Statement] I have been working on a sound a classification problem with less 200 sound files (wav format) with the following imbalanced class distribution: ...
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2answers
38 views

Is my model overfitting?

I'm currently building my first model with sklearn to predict whether a customer will renew a subscription. I'm using a random forest because I've heard that they are robust to overfitting. The ...
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1answer
34 views

How can I find if it is an overfitting problem?

I am new in Machine learning, and I want to detect emotions from the face. Preprocessing: I used equalizeHist to equalizes the histogram of grayscale images (JAFFE database with 213 images), in the ...
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1answer
28 views

model selection in clustering

I am working on a mall customer segmentation dataset (5 features, 200 rows) using clustering. This dataset does not have any ground truth labels. I had a few doubts regarding clustering: Can I use ...
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3answers
1k views

Overfitting in Linear Regression

I'm just getting started with machine learning and I have trouble understanding how overfitting can happen in a linear regression model. Considering we use only 2 feature variables to train a model, ...
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67 views

Does gradient boosting algorithm error always decrease faster and lower on training data?

I am building another XGBoost model and I'm really trying not to overfit the data. I split my data into train and test set and fit the model with early stopping based on the test-set error which ...
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Autoencoder fails to reconstruct

I'm trying to use an autoencoder to reduce dimensionality of my features. My features are of dimension 2048. I tried to train an autoencoder to reduce the dimensionality to 50. I'm using a single ...
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0answers
38 views

What is cross validation good for, exactly?

I keep seeing that cross validation is a good way to reduce overfitting, but, in my case, I don't see how it helps much. Let me explain: I'm interested in running a Multinomial Logit for prediction, ...
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23 views

CNN is not learning anything

I'm training a CNN network to detect relations between entities in written texts. I am suffering from an overfitting problem, I have high accuracy and low loss at the training step, but my model can't ...
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17 views

Escaping from overfitting hell: introducing regularization vs increasing training data

I am trying to identify noisy intervals in geomagnetic data using logistic regression, working with scikit-learn. Here is a typical spectrum of the data that I am working with: In this example, the ...
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2answers
384 views

Can a linear regression model without polynomial features overfit?

I've read in some articles on the internet that linear regression can overfit. However is that possible when we are not using polynomial features? We are just plotting a line trough the data points ...
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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 ...
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30 views

Data augmentation for recommendation systems

I have a user-item matrix that I use to train a denoising autoencoder to predict the top-k items to recommend to the different users. The idea is to corrupt the matrix by erasing a percentage ...
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1answer
188 views

What is Happening in the training process when we are fitting a model to the data [closed]

In any prediction task, the process of “fitting” a model to the data observed in the training process can be best described as... Assessing all observations available and then backsolving for the ...
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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 ...
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1answer
16 views

Does training of neural networks follow the same order in each epoch?

Each epoch uses the weight from the end of the previous epoch(correct me if I am wrong). Is the updating of parameters after each batch always in the same order? To rephrase, are the batches always in ...
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1answer
24 views

Augmenting the validation set in Ensemble Model

I have 8 models which I have trained on 90% of my set (training set) and tracked its performance on the loss of the validation set (10% of the original set). I want to generate an ensemble model by ...
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19 views

Why Overfitting sometimes appears when compile model multiple time, is it normal?

At the time I got small datasets of brainwaves (EEG) (105 samples) for 3-class classification problem. I split my data into 3 part: Train data = 90 (data) Validation data = 10 (data) Test data = 5 (...
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25 views

Do non-parametric models always overfit without regularization?

Let's scope this to just classification. It's clear that if you fully grow out a decision tree with no regularization (e.g. max depth, pruning), it will overfit the training data and get full accuracy ...
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1answer
43 views

regularization error vs over fitting

I have a collected data from 50 unique blocks, and then merged data from 49 blocks into one data set, and saved the data from 1 block for testing purpose. I then split the merged data set from 49 ...
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2answers
39 views

Why cant I overfit this dataset with my neural network?

I have read that given a model is complex enough and I train for enough epochs, my model should at some point overfit the dataset. However I implemented a simple neural network in keras and my ...
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0answers
41 views

weight decay in ResNet50

Can someone please guide for implementing weight decay in transfer learning approach? I want to regularize the pre-trained model ResNet50, where I'm fine-tuning the model for an image classification ...
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1answer
58 views

How to reduce overfitting in a pre-trained network

I have a custom dataset with 10 classes and I am using a pre-trained resnet18 model from torch-vision. I can clearly see it's over-fitting because: the model is trained for 75 epochs with a batch size ...
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5answers
244 views

How many ways are there to check model overfitting?

I am running xgboost on a regression classification problem where the model is predicting a score of how likely a gene is to cause a disease from 0-1. I try to avoid overfitting in all the ways I can ...
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Why is my time series model predicting strange results?

I am trying to predict some time-series data. The output data predicts two numbers (one that's usually greater than 1 and another that is usually less than 1). I've plotted about 800 samples where the ...
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How can a CNN account for spectro-temporal constraints in neural data?

What are there the best ways to leverage the unique "geometrical" constraints of spectro-temporal signal representations (architecture, filter shapes, data augmentation, etc.)? For example, ...
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1answer
18 views

How to analyse the results of cross-validation do determine overfitting

I performed k-fold CV and measured the resulting average error (RMSE) for each fold. This was done with 5 folds, and 4 of the measurements gave similar errors (between 10% and 12%), but one of the ...
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2answers
70 views

When using Scikit Learn Grid Search, why are my train and cv scores high, but my test score is a lot lower?

I'm using scikit learn to run some models, and am very confused as to why my test score is so much lower than my cv score and my train score. At the start, I do a 80-20 train-test split. On the train ...
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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. ...
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2answers
93 views

How to handle Overfitting

I am working on machine learning classification problem with two classes (0/1). I would like to build a prediction model. The problem is that I have a small dataSet of ...
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1answer
46 views

Is this overfitting?

I read about the validations curves, and the following plot is similar to overfit, but in this case, the validation curve doesn't' growth again. So is this overfit? why? Thanks
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1answer
62 views

How to train a keras model on both original and augmented data from ImageDataGenerator?

I have a dataset that contains about 87000 images in a directory, with each class in a separate subfolder. I've tried the class ImageDataGenerator() and the ...
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1answer
101 views

Overfitting in Huggingface's TFBertForSequenceClassification

I'm using Huggingface's TFBertForSequenceClassification for multilabel tweets classification. During training the model archives good accuracy, but the validation accuracy is poor. I've tried to solve ...
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2answers
49 views

Mathematically prove why sparsity leads to model overfitting

With respect to the stackoverflow post here: https://stackoverflow.com/a/59566478/9130959 I can't quite get why the logic stands: when # features increases, the hypothesis space is expanded, leading ...
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1answer
38 views

Strange Neural Network overfitting

I'm experiencing a very strange behavior in training the following NN model for multiclass classification: ...
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0answers
31 views

XGBOOST/lLightgbm over-fitting despite no indication in cross-validation test scores?

We currently work on a project where we aim to identify a set of predictors that may influence the risk of a relatively rare outcome. We are using a semi-large clinical dataset, with data on nearly ...
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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. ...
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1answer
9 views

Tuning SVM C parameter

I would like to ask for help regarding my model. I have a dataset of preprocessed images and I performed a binary classification with SVM on Python. I tuned the value of the c parameter from 0.001 to ...
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3answers
145 views

Disparity between training and testing errors with deep learning: the bias-variance tradeoff and model selection

I am developing a convolutional neural network and have a dataset with 13,000 datapoints that is split 80%/10%/10% train/validation/test. In tuning the model architecture, I found the following, after ...
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clustering more than optimal k and Overfitting in k-means

In my data by using elbow method. i got optimal k to be 3. but , i clustered them into 5 clusters.and the patterns in the cluster are as i wanted them . But, does using k more than optimal k decreases ...
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1answer
16 views

How to prevent neural network from overfitting on small subset of features

I'm trying to predict the win probability for a team in a basketball game using a neural network with a single sigmoid output. The input layer consists of a one-hot representation of the players, ...

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