Regularization Term
Overfitting in Polynomial Regression, Comparing the training set's root mean squared error and the validation set's root-mean-squared-error.
Graph of the root-mean-square-error vs lnλ for the M=9 polynomial
I didn't understand this graph properly. While training the model to learn the parameters, we have to set λ = 0 since it doesn't make sense to already select the value of λ and then proceed with the training. So How is the training error varying as we vary the value of λ?. We divided the dataset into the valid and the train so that we train the model in the training set, and then verify the validation through the valid set.
You may confuse concepts here.
Why the loss function?
You apply a shrinkage penalty to your loss function.
This will nudge your model towards finding weights closer to zero. Which can e.g. be helpful to do regularization by trading variance with bias (see e.g. the Ridge Regression in “An Introduction to Statistical Learning” by Witten et al.)
How to train?
Setting the term λ after training should not affect your trained model. Think about it in terms of linear regression: as soon as you have fitted the linear regression function, your error function does not matter anymore. You just apply the linear regression function.
Thus, you have to set the λ parameter during training.
Otherwise, your model will optimize the parameters without the regularization term and will thus not shrink the sum of weights. Hence, you are actually training your model without regularization.
How to find a good value for λ?
You have to distinguish multiple steps:
Training: You train a model setting λ to a fixed value on your training data. The λ does not change during training but must not be zero ( if you want to have regularization).
This training will yield a model; let’s call it Model λ1.
Single Validation Run: You validate how well Model λ1 performs on the validation set. This tells you how and if λ improved your model, but not if there is a better λ.
Cross-Validation Idea: Train multiple models and use validation runs to evaluate their performance to find the best λ. In other words, you train multiple models (e.g. Model λ2 .. λ10) with different λ values. Then you can compare their validation set performance among each other, to see which value for λ is best (on the validation set).
Why having a 3 set split ( train / validation / test): If you pick a final model with this procedure (e.g. Model λ3) you still don’t know how well your model generalizes because you have been using the validation set to find a good value for λ. Thus, you would expect your model to perform rather well on the validation set.
Hence, you evaluate your final model on the test set which your model has never seen, and where you never performed any kind of parameter optimization. The performance you measure is the final performance of your model. It is important that you do not evaluate multiple models on the training set, and then select the best because, then, again you would be optimizing the performance on the training set.
How to interpret the plot?
This is actually hard without some more knowledge about the problem you are tackling.
On the first look, it seems like your model is overfitting for small values of λ and improves its performance on the validation set for larger values due to regularization.
Related
As I learned about cross-validation algorithm, from most of the articles on the web, there are variety of cross-validation methods. Here I want to be clear about the k-fold cross-validation technique.
In the k-fold cross-validation algorithm, we can split the training set in to k not-overlapped folds.
As we split the training data in to k folds, we have to train the model in k iterations.
So, in each iteration, we train the model with (k-1) folds and validate it with the remained fold.
In each split we can calculate the desired metric(s) of our model.
At the end we can report the training error by taking the average of scores of all iterations.
But what is the final trained model?
Some points in those articles are not clear for me?
Should I initiate model's parameters in each iteration?
I ask this, because if I don’t initialize the parameter's it could save the pattern of data which I want to be unseen in the next iteration and so on…
Should I save the initial parameter of the split in which I gained the best score, as the best initial values of the parameters?
Should I retrain the model initiating it with the initial values of the parameters gained in my second question and then feed it with whole training dataset and gain the final trained model?
Alright so before answering your question I will go a bit back to explain the purpose of cross validation and model evaluation. You can read these slides or research more about statistical learning theory if you want to go deeper.
Train/test split
Suppose you have a model with defined hyperparameter (or none) and you train it on the training split. If you calculate the metrics over the test split, this will give you the risk of the model on new data. Then you know that this particular model will perform like that on unseen data.
So we have a learning process B, that takes a dataset S (here the training dataset) as well as hyperparameters h, and gives a fitted model m; then B(S, h)->m (training B on S with hp h gives a model m, with its parameters). Then we tested this model to evaluate the risk R on the test dataset.
k-fold Cross validation
When doing k-fold cross validation, you fit k models using the learning process B. Each model is fitted on a different training set, and the risk is computed on non overlapping samples.
Then, you calculate the mean risk among the folds. A common mistake is that it gives you the performance of the model, that's not true. This gives you the mean (or expected) performances of the learning process B (and hyperparams h). That means, if you train a new model using B (and hyperparams h), its expected performance will be around the calculated metrics (of course this is not always true).
For your questions
Yes you should train the model from scratch, if possible with the same initial parameters (if initialization is not random) to avoid any difference between folds. Using a warm start with the previous parameters can modify the learning process, and the fitting.
No, if initialization is random let it be, if it is fixed use the same initial parameters for all folds
For the two previous questions, if by initial parameters you meant hyperparameters, then you should keep the same for all folds, otherwise the calculated risk will be useless. If you want to try multiple hyperparameters, you have to repeat the cross validation multiple times, and then you can select the best ones based on the risk calculated.
Once you tuned your hyperparameters you can train the model on your whole training set. This will give you a model m. Before your cross validation you can keep a small test split to evaluate this final model on unseen data
What is 'fit' in machine learning? I noticed in some cases it is a synonym for training.
Can someone please explain in layman's term?
A machine learning model is typically specified with some functional form that includes parameters.
An example is a line intended to model data that has an outcome variable y that can be described in terms of a feature x. In that case, the functional form would be:
y = mx + b
fitting the model means finding values for m and b that are in accordance with training data, which is a set of points (x1, y1), (x2, y2), ..., (xN, yN). It may not be possible to set m and b such that the line passes through all training data points, but some loss function could be defined for describing a well-fit line. The fitting algorithm's purpose would be to minimize that loss function. In the case of line fitting, the loss could be the total distance of training data points to the line, but it may be more mathematically convenient to set the loss to the total squared distance of training data points to the line.
In general, a model can be more complex than a line and include many parameters. For some models, the number of parameters is not fixed and can change as part of the fitting process. The features and the outcome variable can be discrete, continuous, and/or multidimensional. For unsupervised problems, there is no outcome variable.
In all these cases, fitting is still analogous to the line example above, where an algorithm is run to find model parameters that in some sense explain the training data. This often involves running some optimization procedure.
A model that is well-fit to the training data may not be well-fit to other non-training data, even if the other data is sampled from the same distribution as the training data. A technique called regularization can be used to address this issue.
I trying to learn about decision trees (and other models) and I came across cross validation, now I first thought that cross validation was used to determine the optimal parameters for the model. For example the optimal max_tree_depth in decision tree classification or the optimal number_of_neighbors in k_nearest_neighbor classification. But as I am looking at some examples I think this might be wrong.
Is this wrong?
Cross-validation is used to determine the accuracy of your model in a more accurate way for example in a n-fold cross validation you divide you data into n partitions and use n-1 parts as train set and 1 part as test set and repeat this for all partitions each partition gets to be test set once) then you average results to get a better estimation of your model's accuracy
I was wondering if a model trains itself from the test data as well while evaluating it multiple times, leading to a over-fitting scenario. Normally we split the training data into train-test splits and I noticed some people split it into 3 sets of data - train, test and eval. eval is for final evaluation of the model. I might be wrong but my point is that if the above mentioned scenario is not true, then there is no need for an eval data set.
Need some clarification.
The best way to evaluate how well a model will perform in the 'wild' is to evaluate its performance on a data set it has not seen (i.e., been trained on) -- assuming you have the labels in a supervised learning problem.
People split their data into train/test/eval and use the training data to estimate/learn the model parameters and the test set to tune the model (e.g., by trying different hyperparameter combinations). A model is usually selected based on the hyperparameter combination that optimizes a test metric (regression - MSE, R^2, etc.; classification - AUC, accuracy, etc.). Then the selected model is usually retrained on the combined train + test data set. After retraining, the model is evaluated based on its performance on the eval data set (assuming you have some ground truth labels to evaluate your predictions). The eval metric is what you report as the generalization metric -- that is, how well your model performs on novel data.
Does this help?
Consider you have train and test datasets. Train dataset is the one in which you know the output and you train your model on train dataset and you try to predict the output of Test dataset.
Most people split train dataset into train and validation. So first you run your model on train data and evaluate it on validation set. Then again you run the model on test dataset.
Now you are wondering how this will help and of any use?
This helps you to understand your model performance on seen data(validation data) and unseen data(your test data).
Here comes bias-variance trade-off into picture.
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/
Let's consider a binary classification example where a student's previous semester grades, Sports achievements, Extracurriculars etc are used to predict whether or not he will pass the final semester.
Let's say we have around 10000 samples (data of 10000 students).
Now we split them:
Training set - 6000 samples
Validation set - 2000 samples
Test set - 1000 samples
The training data is generally split into three (training set, validation set, and test set) for the following reasons:
1) Feature Selection: Let's assume you have trained the model using some algorithm. You calculate the training accuracy and validation accuracy. You plot the learning curves and find if the model is overfitting or underfitting and make changes (add or remove features, add more samples etc). Repeat until you have the best validation accuracy. Now test the model with the test set to get your final score.
2) Parameter Selection: When you use algorithms like KNN, And you need to find the best K value which fits the model properly. You can plot the accuracy of different K value and choose the best validation accuracy and use it for your test set. (same applies when you find n_estimators for Random forests etc)
3) Model Selection: Also you can train the model with different algorithms and choose the model which better fits the data by testing out the accuracy using validation set.
So basically the Validation set helps you evaluate your model's performance how you must fine-tune it for best accuracy.
Hope you find this helpful.
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.