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i have this model that i trained with 100 epochs :
Model with 100 Epoch
and then i save the model and train for another 100 epoch (total 200 epoch):
Model with additional 100 epoch (200 epoch)
my question is, is my model not overfitting ? is it optimal ?
Overfitting is when a model captures patterns that won't recur in the future. This leads to a decrease in prediction accuracy.
You need to test your model on data that has not been seen in training or validation to determine if it is overfitting or not.
Over fitting is when your model scores very highly on your training set and poorly on a validation test set (or real life post-training predictions).
When you are training your model make sure that you have split your training dataset into two subsets. One for training and one for validation. If you see that your validation accuracy is decreasing as training goes on it means that your CNN has "overfitted" to the training set specifically and should not be generalized.
There are many ways to combat overfitting that should be used while training your model. Seeking more data and using harsh dropout are popular ways to ensure that a model is not overfitting. Check out this article for a good description of your problem and possible solutions.
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I have a question regarding the model.fit method and overfitting from the scikit learn library in Pandas
Does the generic sklearn method model.fit(x---, y--) returns the score after applying the model to the specified training data?
Also, it is overfitting when performance on the test data degrades as more training data is used to learn the model?
model.fit(X, y) doesn't explicitly give you the score, if you assign a variable to it, it stores all the artifacts, training parameters. You can get the score by using model.score(X, y).
Overfitting in simple words is increasing the variance in your model by which your model fails to generalize. There are ways to reduce overfitting like feature engineering, normalization, regularization, ensemble methods etc.
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I readed this post and i get a little understanding of how to interpret
loss: 0.1350 - accuracy: 0.7074 - val_loss: 0.1711 - val_accuracy: 0.5299
but in my case loss decreases and accuracy increases but val_loss and val_accuracy just stay in the same range:
val_loss goes from 0.1511 to 0.2011
val_accuracy goes from 0.4011 to 0.7011
what does this means?
Loss and accuracy refer to how well your model performed on the training data. Whereas validation loss and validation accuracy refer to how well your model performed on the validation set, which is not part of the training data. You describe a situation where your model is doing better and better on training data, but not doing better and better on validation data. This means that your model is essentially learning to predict the training data instead of learning the underlying concept to generalize. You may need to consider tweaking the parameters of your model or choose a different model altogether.
It means your model is performing well on your training dataset, but not on your validation (test) dataset. This might mean your training dataset is not a very good sample for your optimisation task. Or that your training set and test set model different functions altogether.
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I have written a ML-based Intrusion prediction. In the learning process, I used training and test data both labeled to evaluate the accuracy and generate confusion matrixes. I came up with good accuracy and now I want to test it with new data( Unlabeled data). How do I do that?
Okay so say you do test on unlabeled data and your algorithm predicts some X output. How can you check the accuracy, how can you check if this is correct or not? This is the only thing that matters in predictions, how your program works on data it has not seen before.
The short answer is, you can't. You need to split your data into:
Training 70%
Validation 10%
Test 20%
All of these should be labled and accuracy, confusion matrix, f measure and anything else should be computed on the labled test data that your program has not seen before. Your train on training data and every once in a while you check the performance on the validation data to see if it is doing well or if you need to do adjustments. In the very end you check on test data. This is supervised learning, you always need labeled data.
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How to determine what's the optimal number of iterations in learning a neural network?
One way of doing it is to split your training data into a train and validation set. During training, the error on the training set should decrease steadily. The error on the validation set will decrease and at some point start to increase again. At this point the net starts to overfit to the training data. What that means is that the model adapts to the random variations in the data rather than learning the true regularities. You should retain the model with overall lowest validation error. This is called Early Stopping.
Alternatively, you can use Dropout. With a high enough Dropout probability, you can essentially train for as long as you want, and overfitting will not be a significant issue.
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Can testing_set be used as validation_set ?
Does validation_set have any affect on model learning or used for checking validation accuracy only for each epoch ?
I am using keras library for the building model .
model.fit(X_train, Y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(X_test, Y_test))
You use validation set to figure out how much you overfit and to decide when to stop learning. So to get a more or less "independent" quality measure of your model you need another set of data, which is test set.
Please refer to following discussion for more information.
If you're using Keras you can pass validation_split parameter to the model, so that Keras splits train set data for you.
For a test set to be a true test set, the labels should never be supplied to the model. If you use the test set also as the validation set, then although your model isn't necessarily training on the validation set, it will have seen the labels for this set during training
So, in short, you really need three distinct sets of data for training, validation, and test.
If you need additional resources, here is a video that breaks these sets down into their distinct purposes, and here is another one for working with validation sets in Keras.