What is the difference between the Holdout dataset and the Validation dataset in Machine Learning context?
The Validation dataset is used during training to track the performance of your model on "unseen" data. I wrote the unseen in quotes because although the model doesn't directly see the data in validation set, you will optimize the hyper-parameters to decrease the loss on validation set (since increasing val loss will mean over-fitting). However, by doing so, you may over-fit the hyper-parameters to validation set (So that the loss will be low on that specific validation set, but will become worse on any other unseen set). That's why you usually keep another 3rd set, called test set (or held-out set), which will be your truly unseen data, and you will test the performance of your model on that test set only once, after training your final model.
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Let's say I initially split my dataset into training (80%) and test (20%) sets, perform a 10-fold CV on my training set and obtain an average R² of 75%. After that I check the best model's accuracy on the test set and obtain an R² of 74%, which indicates that the model is fairly robust. Now, before deploying it to real applications, I tune it with the whole data. Someone asks me the model's approximate R²; if I say 74% or 75%, I will me ignoring the fact that the model was now tunned with more data (test set). Is it a resonable approach to perform a leave one out CV on the chosen model with the whole data, compare the predicted targets with the real ones, check the R² (let's say it's 80% now) and say that the real-world model will most likely have an R² of 80%? I see no problems with that, but I do not know if this approach is correct.
It is true that you should train again on the whole data and it might lead to performance improvements. However, in this context your whole data should not be train + test! It should be just the trainign dataset but without any cross-validation. So before you had %80 for training and you were doing 10 fold CV, meaning that you were training your model actually on %72 of your complete data(train+test) and keeping the %8 for the validation. Now you should train it on the whole %80 percent and report your final results again on the unseen test set.
If you do LOOCV on the train + test, you can not report your performance on validation samples because this is how the model is finetuned and you might as well overfit to validation data.
I'm referring to the validation_split parameter from the fit method from Keras:
validation_split: Float between 0 and 1. Fraction of the training data
to be used as validation data. The model will set apart this fraction
of the training data, will not train on it, and will evaluate the loss
and any model metrics on this data at the end of each epoch. The
validation data is selected from the last samples in the x and y data
provided, before shuffling.
I noticed that the default value is 0 instead of conventional 0.2 or 0.33. I can't wrap my head around why they chose to use 0 as the default value since I thought no validation set would always cause training to overfit. Am I wrong in that assumption?
A validation set is used to detect overfitting, not having a validation set just means that you cannot detect overfitting. It does not mean that the model will automatically overfit. Remember that validation data is not used at all to train the model, so the model cannot possibly behave differently if validation data is not being used.
That said, having a default of no validation set makes sense, because in the end is a human who detects overfitting by looking at the learning curves and the difference between training and validation loss. This process cannot (currently) be automated, so a human has to decide a value for the validation split, or just provide validation data by itself in the validation_data parameter.
Sometimes you want to define the validation data yourself, and you pass the argument validation_data= (x_val, y_val)
Sometimes you want a K-fold cross validation.
Sometimes you simply don't want validation data.
The system cannot assume your training data includes validation, that is not a good thing for the user.
As for overfitting, it depends on the model and the data. It's not necessarily true that it will always overfit.
I split the dataset into train and test of 80-20 ration respectively. I predicted and evaluated with test dataset. And my question is can we evaluate and predict model with the whole dataset before that I shuffle entire dataset. Can we do that? If not, why should not we do that? what is wrongdoing like that?
Data Snooping is the quick answer what you are looking for.
In other words, your model would seem outperforming on your test data if it was trained on 100% data first. Model would become an overfitted model that basically would predict seen data with higher accuracy however would fail to do so with any sort of unseen test data.
You can do it, however it would result in overfitted model. You can try k fold cross validation method in stead.
If you use the whole dataset for training, the model will fit to all the variances in data (overfitting). As a result, the performance of your model on similar data will be high. However, the model will exhibit low performance on unseen data with a different distribution compared to your training dataset. One way to prevent this is to: a) split your data into training, validation, and testing datasets (see the note below), b) apply k-fold cross-validation on training and validation splits, c) verify the performance of your models from step b on the third split (test dataset).
Note: There is no consensus on the naming of the splits. Some sources name them training-validation-testing while others use training-testing-validation.
Suppose I split my data into training set and validation set. I perform a 5-fold cross-validation on my training set to obtain the optimal hyper-parameters for my model, then I use the optimal hyper-parameters to train my model and apply the resulting model on my validation set. My question is, is it reasonable to combine the training and validation set, and use the hyper-parameters obtained from the training set to build a final model?
It is resonable if training data was relatively small and adding validation set makes your model significantly stronger. However, at the same time, adding new data makes your previously selected hyperparameters possibly suboptimal (it is really hard to show what kind of transformation of hyperparameters you should apply when you add new data to your training set). Thus you balance two things - gain in model quality from more data and possible loss due to hard to predict change in hyperparameters meaning. To some extent you can simulate this process to make sure it makes sense, if you have N points in training data and M in validation, you can try to split training further to chunks with the same proportion (thus one is now N * (N/(N+M) and other N * (M/(N+M))), train on first one and check whether optimal hyperparameters transfer (more or less) to the optimal one on the whole training set - if so, you can safely add validation as they should transfer as well. If they do not - the risk might be not worth the gain.
When developing a neural net one typically partitions training data into Train, Test, and Holdout datasets (many people call these Train, Validation, and Test respectively. Same things, different names). Many people advise selecting hyperparameters based on performance in the Test dataset. My question is: why? Why not maximize performance of hyperparameters in the Train dataset, and stop training the hyperparameters when we detect overfitting via a drop in performance in the Test dataset? Since Train is typically larger than Test, would this not produce better results compared to training hyperparameters on the Test dataset?
UPDATE July 6 2016
Terminology change, to match comment below. Datasets are now termed Train, Validation, and Test in this post. I do not use the Test dataset for training. I am using a GA to optimize hyperparameters. At each iteration of the outer GA training process, the GA chooses a new hyperparameter set, trains on the Train dataset, and evaluates on the Validation and Test datasets. The GA adjusts the hyperparameters to maximize accuracy in the Train dataset. Network training within an iteration stops when network overfitting is detected (in the Validation dataset), and the outer GA training process stops when overfitting of the hyperparameters is detected (again in Validation). The result is hyperparameters psuedo-optimized for the Train dataset. The question is: why do many sources (e.g. https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf, Section B.1) recommend optimizing the hyperparameters on the Validation set, rather than the Train set? Quoting from Srivasta, Hinton, et al (link above): "Hyperparameters were tuned on the validation set such that the best validation error was produced..."
The reason is that developing a model always involves tuning its configuration: for example, choosing the number of layers or the size of the layers (called the hyper-parameters of the model, to distinguish them from the parameters, which are the network’s weights). You do this tuning by using as a feedback signal the performance of the model on the validation data. In essence, this tuning is a form of learning: a search for a good configuration in some parameter space. As a result, tuning the configuration of the model based on its performance on the validation set can quickly result in overfitting to the validation set, even though your model is never directly trained on it.
Central to this phenomenon is the notion of information leaks. Every time you tune a hyperparameter of your model based on the model’s performance on the validation set, some information about the validation data leaks into the model. If you do this only once, for one parameter, then very few bits of information will leak, and your validation set will remain reliable to evaluate the model. But if you repeat this many times—running one experiment, evaluating on the validation set, and modifying your model as a result—then you’ll leak an increasingly significant amount of information about the validation set into the model.
At the end of the day, you’ll end up with a model that performs artificially well on the validation data, because that’s what you optimized it for. You care about performance on completely new data, not the validation data, so you need to use a completely different, never-before-seen dataset to evaluate the model: the test dataset. Your model shouldn’t have had access to any information about the test set, even indirectly. If anything about the model has been tuned based on test set performance, then your measure of generalization will be flawed.
There are two things you are missing here. First, minor, is that test set is never used to do any training. This is a purpose of validation (test is just to asses your final, testing performance). The major missunderstanding is what it means "to use validation set to fit hyperparameters". This means exactly what you describe - to train a model with a given hyperparameters on the training set, and use validation to simply check if you are overfitting (you use it to estimate generalization) , but you do not really "train" on them, you simply check your scores on this subset (which, as you noticed - is way smaller).
You cannot "stop training hyperparamters" because this is not a continuous process, usually hyperparameters are just "possible sets of values", and you have to simply test lots of them, there is no valid way of defining a direct trainingn procedure between actual metric you are interested in (like accuracy) and hyperparameters (like size of the hidden layer in NN or even C parameter in SVM), as the functional link between these two is not differentiable, is highly non convex and in general "ugly" to optimize. If you can define a nice optimization procedure in terms of a hyperparameter than it is usually not called a hyperparameter but a parameter, the crucial distinction in this naming convention is what makes it hard to optimize directly - we call hyperparameter a parameter, than cannot be directly optimized against thus you need a "meta method" (like simply testing on validation set) to select it.
However, you can define a "nice" meta optimization protocol for hyperparameters, but this will still use validation set as an estimator, for example Bayesian optimization of hyperparameters does exactly this - it tries to fit a function saying how well is you model behaving in the space of hyperparameters, but in order to have any "training data" for this meta-method, you need validation set to estimate it for any given set of hyperparameters (input to your meta method)
simple answer: we do
In the case of a simple feedforward neural network you do have to select e.g. layer and unit count per layer, regularization (and non-continuous parameters like topology if not feedforward and loss function) in the beginning and you would optimize on those.
So, in summary you optimize:
ordinary parameters only during training but not during validation
hyperparameters during training and during validation
It is very important not to touch the many ordinary parameters (weights and biases) during validation. That's because there are thousands of degrees of freedom in them which means they can learn the data you train them on. But then the model doesn't generalize to new data as well (even when that new data originated from the same distribution). You usually only have very few degrees of freedom in the hyperparameters which usually control the rigidity of the model (regularization).
This holds true for other machine learning algorithms like decision trees, forests, etc as well.