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29 votes
Accepted

Is a 100% model accuracy on out-of-sample data overfitting?

High validation scores like accuracy generally mean that you are not overfitting, however it should lead to caution and may indicate something went wrong. It could also mean that the problem is not ...
Jan van der Vegt's user avatar
26 votes

Overfitting in Linear Regression

In linear regression overfitting occurs when the model is "too complex". This usually happens when there are a large number of parameters compared to the number of observations. Such a model ...
Robert Long's user avatar
25 votes
Accepted

When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?

I am unsure there will be a formal way to show which is best in which situations - simply trying out different combinations is likely best! It is worth noting that Dropout actually does a little bit ...
n1k31t4's user avatar
  • 14.9k
21 votes

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

There are a couple of nuances here. Complexity question very important - ocams razor CV - is this trully the case 84%/83% (test it for train+test with CV) Given this, personal opinion: Second one. ...
Noah Weber's user avatar
  • 5,699
20 votes
Accepted

Can the number of epochs influence overfitting?

Yes, it may. In machine-learning there is an approach called early stop. In that approach you plot the error rate on training and validation data. The horizontal axis is the ...
Green Falcon's user avatar
  • 14.1k
20 votes
Accepted

In which epoch should i stop the training to avoid overfitting

As long as your validation accuracy increases, you should keep training. I would stop when the test accuracy starts decreasing (this is known as early stopping). The general advise is always to keep ...
David Masip's user avatar
  • 6,101
20 votes
Accepted

Is over fitting okay if test accuracy is high enough?

If you properly isolate your test set such that it doesn't affect training, you should only look at the test set accuracy. Here are some of my remarks: Having your model being really good on the ...
Valentin Calomme's user avatar
15 votes
Accepted

How to know if a model is overfitting or underfitting by looking at graph

Overfitting is a scenario where your model performs well on training data but performs poorly on data not seen during training. This basically means that your model has memorized the training data ...
zachdj's user avatar
  • 2,734
15 votes

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

It depends mostly on the problem context. If predictive performance is all you care about, and you believe the test set to be representative of future unseen data, then the first model is better. (...
Ben Reiniger's user avatar
  • 11.9k
15 votes
Accepted

Why does my model produce too good to be true output?

Assuming that these results are obtained on a valid test set with no data leakage, these results don't show overfitting because overfitting would cause great performance on the training set but ...
Erwan's user avatar
  • 25.5k
14 votes

High model accuracy vs very low validation accuarcy

When a machine learning model has high training accuracy and very low validation then this case is probably known as over-fitting. The reasons for this can be as follows: The hypothesis function you ...
Syed Nauyan Rashid's user avatar
12 votes
Accepted

Dropout vs weight decay

These techniques are not mutually exclusive; combining dropout with weight decay has become pretty standard for deep learning. However, where weight decay applies a linear penalty, dropout can cause ...
Ben's user avatar
  • 2,572
11 votes
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Can overfitting occur in Advanced Optimization algorithms?

There is no technique that will eliminate the risk of overfitting entirely. The methods you've listed are all just different ways of fitting a linear model. A linear model will have a global minimum, ...
Ryan Zotti's user avatar
  • 4,149
11 votes
Accepted

High model accuracy vs very low validation accuarcy

You should try to shuffle all of your data and split them to the train and test and valid set then train again.
Anh Phạm's user avatar
11 votes
Accepted

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

Which two accuracies I compare to see if the model is overfitting or not? You should compare the training and test accuracies to identify over-fitting. A training accuracy that is subjectively far ...
Esmailian's user avatar
  • 9,342
10 votes
Accepted

Can overfitting occur even with validation loss still dropping?

I am not sure if the validation set is balanced or not. You have a severe data imbalance problem. If you sample equally and randomly from each class to train your network, and then a percentage of ...
Bashar Haddad's user avatar
9 votes

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

The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. Maybe not. It's true that 100% training accuracy is usually a strong indicator of overfitting, but it's ...
Ray's user avatar
  • 191
9 votes
Accepted

How many ways are there to check model overfitting?

The direct way to check your model for overfitting is to compare its performance on a training set with its performance on a testing set; overfitting is when your train score is significantly above ...
Itamar Mushkin's user avatar
8 votes
Accepted

Is Overfitting a problem in Unsupervised learning?

Overfitting happens when the model fits the training dataset more than it fits the underlying distribution. In a way, it models the specific sample rather than producing a more general model of the ...
DaL's user avatar
  • 2,643
8 votes

How to know the model has started overfitting?

Let's say we want to predict if a student will land a job interview based on her resume. Now, assume we train a model from a dataset of 10,000 resumes and their outcomes. Next, we try the model out ...
dileep balineni's user avatar
8 votes

Validation loss increases and validation accuracy decreases

What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science. Overfitting happens when a model begins to focus on the noise in the training data ...
Mark.F's user avatar
  • 2,230
8 votes
Accepted

Which method is more suitable? overfitting of traning data or low accuracy?

The performance on in-sample data almost does not count. The performance on out-of-sample data is more indicative of how you should expect your model to perform on future inputs. The second model has ...
Dave's user avatar
  • 3,960
7 votes

Why doesn't overfitting devastate neural networks for MNIST classification?

I have replicated your results using Keras, and got very similar numbers so I don't think you are doing anything wrong. Out of interest, I ran for many more epochs to see what would happen. The ...
Neil Slater's user avatar
7 votes
Accepted

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

Your dataset is highly imbalanced. Your optimization process is just minimizing the loss function, and cannot do better than a model that predicts uninteresting regardless of the input, due to the ...
David Masip's user avatar
  • 6,101
7 votes

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

To deal with overfitting, you need to use regularization during the training: Weight regularization - The first thing you have to do (practically always) is to use regularization on the weights of ...
Mark.F's user avatar
  • 2,230
7 votes
Accepted

ROC AUC score is much less than average cross validation score

Your test score is incorrect. The ROC curve needs the probability scores from the model, not the class decisions. So replace ...
Ben Reiniger's user avatar
  • 11.9k
7 votes
Accepted

Interpretation for test score , training score and validation score in machine learning?

Interpretation for test score , training score and validation score ? what they actually tell us? We usually divide our data-set in 3 parts. Training-data, validation-data and test-data. Then we ...
Ashraful Alam Imran's user avatar
7 votes
Accepted

Imbalanced Dataset: Train/test split before and after SMOTE

Essentially applying SMOTE makes the job easier for the model: SMOTE generates artificial instances which tend to have the same properties as each other, so it's easier for the model to capture their ...
Erwan's user avatar
  • 25.5k
6 votes
Accepted

How to determine if my GBM model is overfitting?

The term overfitting means the model is learning relationships between attributes that only exist in this specific dataset and do not generalize to new, unseen data. Just by looking at the model ...
Simon Boehm's user avatar
6 votes

Factorization Machine - prevent over fitting

Here are some excerpts from the original paper that I think are key to understanding the question: Instead of using an own model parameter for each interaction, the FM models the interaction by ...
akuiper's user avatar
  • 313

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