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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34
votes
6answers
7k views

What would I prefer - an over-fitted model or a less accurate model?

Let's say we have two models trained. And let's say we are looking for good accuracy. The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an ...
10
votes
2answers
8k views

Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?

I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting. But I am confused that which ...
7
votes
1answer
3k views

Why k-fold cross validation (CV) overfits? Or why discrepancy occurs between CV and test set?

Recently, I was working on a project and found my cross-validation error rate very low, but the testing set error rate very high. This might indicate that my model is overfitting. Why does my cross-...
7
votes
5answers
10k views

In which epoch should i stop the training to avoid overfitting

I'm working on an age estimation project trying to classify a given face in a predefined age range. For that purpose I'm training a deep NN using the keras library. The accuracy for the training and ...
5
votes
1answer
12k views

Validation loss increases and validation accuracy decreases

I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is ...
2
votes
2answers
5k views

Overfitting in an unsupervised technique

I am trying to understand if over-fitting can happen in an unsupervised technique like kmeans clustering. Could someone help me understand if and how this would happen? Thanks.
12
votes
4answers
9k views

How to know the model has started overfitting?

I hope the following excerpts will provide an insight into what my question is going to be. These are from here. The learning then gradually slows down. Finally, at around epoch 280 the ...
15
votes
2answers
11k views

Can overfitting occur even with validation loss still dropping?

I have a convolutional + LSTM model in Keras, similar to this (ref 1), that I am using for a Kaggle contest. Architecture is shown below. I have trained it on my labeled set of 11000 samples (two ...
5
votes
1answer
15k views

Accuracy and loss don't change in CNN. Is it over-fitting?

My task is to perform classify news articles as Interesting [1] or Uninteresting [0]. My training set has 4053 articles out of which 179 are Interesting. The validation set has 664 articles out of ...
4
votes
2answers
3k views

What are the possible approaches to fixing Overfitting on a CNN?

Currently I am trying to make a cnn that would allow for age detection on facial images. My dataset has the following shape where the images are grayscale. ...
4
votes
1answer
791 views

Overfitting - how to detect it and reduce it?

I have a side project where I am doing credit scoring using R (sample size around 16k for train data and 4k for test data, and also another two 20k data batches for out-of-time validation) with ...
1
vote
2answers
196 views

What to choose: an overfit model with higher evaluation score or a non-overfit model with lower one?

For lack of a better term, overfit here means a higher discrepancy between train and validation score and non-overfit means a lower discrepancy. This "dilemma" just showed in neural network model I'...
8
votes
2answers
640 views

Is over fitting okay if test accuracy is high enough? [duplicate]

I am trying to build a binary classifier. I have tried deep neural networks with various different structures and parameters and I was not able to get anything better than ...
6
votes
1answer
460 views

Should I prevent augmented data to leak to the test/cross validation sets

I have been working with the cats vs dogs dataset from kaggle which consist on 25000 images of cats and dogs labelled accordingly (btw, great dataset, totally recommended!) One of the things I did ...
5
votes
2answers
503 views

Can we use a model that overfits?

I am on a binary classification problem with the AUC metrics. I did a random split 70%, 30% for training and test sets. My first attempts using random forest with default hyper-parameters gave me auc ...
2
votes
1answer
180 views

Feature selection where adding features are deteriorating model

k I am training a kNN classifier with 144 features and graphed the accuracy vs number of features used and got this. What might be the reason for the drops in the accuracy at some points of the graph?...
1
vote
2answers
1k views

validation/training accuracy and overfitting

If we randomly split the data into training data and validation data, and assume the training data and validation data have similar "distributions", i.e. they are both good representations of the ...