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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.
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TensorFlow/Keras has multiple metrics to monitor but where are they defined? Please point to the documentation or github code where those strings have been defined.
tf.keras.callbacks.EarlyStopping(
monitor="val_loss", # <-----
min_delta=0,
patience=0,
verbose=0,
mode="auto",
baseline=None,
restore_best_weights=False,
start_from_epoch=0,
)
Conclusion
There is no documentation nor definition from TF/Keras. We need to figure them out by searching around, picking up bits and pieces from multiple resources. It is considered as a documentation bug.
Terminologies and values to use should have been defined and documented before being used but not have been done for this case.
Please check documentation of ModelCheckpoint:
https://keras.io/api/callbacks/model_checkpoint/
The „monitor“ parameter for EarlyStopping accepts the same possible strings as ModelCheckpoint. You can specify the prefix „val_“, than the metric is computed on validation set. If you don‘t specify this prefix, metric is computed on a training set.
To find the names of all possible metrics (besides „loss“), please see:
https://keras.io/api/metrics/
Each metric has a name parameter, which can be used as value for EarlyStopping ‚monitor‘ parameter.
For models with several outputs you can specify the desired metric to monitor like here:
multi-output keras model with a callback that monitors two metrics
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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 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'm trying to understand how Gridsearchcv's logic works. I looked at here, the official documentation, and the source code, but I couldn't figure out the following:
What is the general logic behind Gridsearchcv?
Clarifications:
If I use the default cv = 5, what are the % splits of the input data
into: train, validation, and test?
How often does Gridsearchcv perform such a split, and how does it decide which observation belong to train / validation / test?
Since cross validation is being done, where does any averaging come into play for the hyper parameter tuning? i.e. is the optimal hyper parameter value is one that optimizes some sort of average?
This question here shares my concern, but I don't know how up-to-date the information is and I am not sure I understand all the information there. For example, according to the OP, my understanding is that:
The test set is 25% of the input data set and is created once.
The union of the train set and validation set is correspondingly created once and this union is 75% of the original data.
Then, the procedure creates 5 (because cv = 5) further splits of this 75% into 60% train and 15% validation
The optimized hyper parameter value is one that optimizes the average of some metric over these 5 splits.
Is this understanding correct and still applicable now? And how does the procedure do the original 25%-75% split?
First your split your data into train and test. The testing set is left out for post training and optimization of the model. The gridsearchcv takes the 75% of your data and splits them into 5 slices. First it trains 4 slices and validates on 1, then takes 4 slices introducing the previously left out set for validation and tests on a new set etc... 5 times.
Then the performance of each run can be seen + the average of them to understand overall how your model behaves.
Since you are doing a gridsearch, the best_params will be saved at the end of your modeling to predict your test set.
So to summarize, the best parameters will be chosen and used for your model after the whole training, therefore, you can easily use them to predict(X_test)
Read more here.
Usually if you don't perform CV, the model will try to optimize its weights with preset parameters and the left out test set, will help to assess the model performance. However, for a real model training, it is highly important to re-split the training data into train and validation, where you use the validation to hypertune the parameters of the model (manually). However, over-hyptertuning the model to get the best performance on the validation set is cheating.
Theoretical K-Folds
More details
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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.