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 ...
- 9,398
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 ...
- 2,053
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 ...
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22
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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.
...
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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 ...
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20
votes
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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 ...
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20
votes
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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 ...
- 6,106
16
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. (...
- 12.3k
15
votes
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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 ...
- 25.9k
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 ...
14
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 ...
- 2,772
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 ...
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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.
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11
votes
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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 ...
- 9,442
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 ...
- 2,019
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 ...
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9
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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 ...
- 1,091
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 ...
- 2,663
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 ...
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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 ...
- 2,250
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 ...
- 4,244
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 ...
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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 ...
- 6,106
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 ...
- 2,250
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
...
- 12.3k
7
votes
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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 ...
7
votes
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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 ...
- 25.9k
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 ...
- 371
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 ...
- 313
6
votes
Accepted
How to check for overfitting with SVM and Iris Data?
You check for hints of overfitting by using a training set and a test set (or a training, validation and test set). As others have mentioned, you can either split the data into training and test sets, ...
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