Cross-validation is used for estimation of model.
I misunderstand the concept. If different part of corpus is used for training per each iteration, then each iteration generates the different model. So, what's validated? Only selected features?
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I have two datasets and I want to train a SVM classification model (fitcsvm) by one of them and then predict labels for the other one. I use 10-fold cross-validation (crossval) to train my model so I have 10 different models. My question is which one of these models are the best for prediction and how can I find that?
here is my code:
Mdl = fitcsvm(trainingData,labels);
CVMdl = crossval(Mdl);
You may have mixed up something here. The function fitcsvm trains a single model and the function crossval validates this single model. It will then return an evaluation value.
In general, you cannot train a model by cross-validation (as it says, it is a validation technique). However, you can use cross-validation to train good models.
What you are looking for is a sort of hyperparameter optimization. Those are methods that automatically train multiple models on a given data set to find the best tuning values for the SVM. Have a look at the docs here
You can turn it on like this
Mdl = fitcsvm(trainingData,labels,'OptimizeHyperparameters','auto')
You may want to use cross-validation to train multiple models with the same tuning parameters but I guess, you'll have to write this yourself. Perhaps this already helps you.
I have a question regarding cross validation in Linear regression model.
From my understanding, in cross validation, we split the data into (say) 10 folds and train the data from 9 folds and the remaining folds we use for testing. We repeat this process until we test all of the folds, so that every folds are tested exactly once.
When we are training the model from 9 folds, should we not get a different model (may be slightly different from the model that we have created when using the whole dataset)? I know that we take an average of all the "n" performances.
But, what about the model? Shouldn't the resulting model also be taken as the average of all the "n" models? I see that the resulting model is same as the model which we created using whole of the dataset before cross-validation. If we are considering the overall model even after cross-validation (and not taking avg of all the models), then what's the point of calculating average performance from n different models (because they are trained from different folds of data and are supposed to be different, right?)
I apologize if my question is not clear or too funny.
Thanks for reading, though!
I think that there is some confusion in some of the answers proposed because of the use of the word "model" in the question asked. If I am guessing correctly, you are referring to the fact that in K-fold cross-validation we learn K-different predictors (or decision functions), which you call "model" (this is a bad idea because in machine learning we also do model selection which is choosing between families of predictors and this is something which can be done using cross-validation). Cross-validation is typically used for hyperparameter selection or to choose between different algorithms or different families of predictors. Once these chosen, the most common approach is to relearn a predictor with the selected hyperparameter and algorithm from all the data.
However, if the loss function which is optimized is convex with respect to the predictor, than it is possible to simply average the different predictors obtained from each fold.
This is because for a convex risk, the risk of the average of the predictor is always smaller than the average of the individual risks.
The PROs and CONs of averaging (vs retraining) are as follows
PROs: (1) In each fold, the evaluation that you made on the held out set gives you an unbiased estimate of the risk for those very predictors that you have obtained, and for these estimates the only source of uncertainty is due to the estimate of the empirical risk (the average of the loss function) on the held out data.
This should be contrasted with the logic which is used when you are retraining and which is that the cross-validation risk is an estimate of the "expected value of the risk of a given learning algorithm" (and not of a given predictor) so that if you relearn from data from the same distribution, you should have in average the same level of performance. But note that this is in average and when retraining from the whole data this could go up or down. In other words, there is an additional source of uncertainty due to the fact that you will retrain.
(2) The hyperparameters have been selected exactly for the number of datapoints that you used in each fold to learn. If you relearn from the whole dataset, the optimal value of the hyperparameter is in theory and in practice not the same anymore, and so in the idea of retraining, you really cross your fingers and hope that the hyperparameters that you have chosen are still fine for your larger dataset.
If you used leave-one-out, there is obviously no concern there, and if the number of data point is large with 10 fold-CV you should be fine. But if you are learning from 25 data points with 5 fold CV, the hyperparameters for 20 points are not really the same as for 25 points...
CONs: Well, intuitively you don't benefit from training with all the data at once
There are unfortunately very little thorough theory on this but the following two papers especially the second paper consider precisely the averaging or aggregation of the predictors from K-fold CV.
Jung, Y. (2016). Efficient Tuning Parameter Selection by Cross-Validated Score in High Dimensional Models. International Journal of Mathematical and Computational Sciences, 10(1), 19-25.
Maillard, G., Arlot, S., & Lerasle, M. (2019). Aggregated Hold-Out. arXiv preprint arXiv:1909.04890.
The answer is simple: you use the process of (repeated) cross validation (CV) to obtain a relatively stable performance estimate for a model instead of improving it.
Think of trying out different model types and parametrizations which are differently well suited for your problem. Using CV you obtain many different estimates on how each model type and parametrization would perform on unseen data. From those results you usually choose one well suited model type + parametrization which you will use, then train it again on all (training) data. The reason for doing this many times (different partitions with repeats, each using different partition splits) is to get a stable estimation of the performance - which will enable you to e.g. look at the mean/median performance and its spread (would give you information about how well the model usually performs and how likely it is to be lucky/unlucky and get better/worse results instead).
Two more things:
Usually, using CV will improve your results in the end - simply because you take a model that is better suited for the job.
You mentioned taking the "average" model. This actually exists as "model averaging", where you average the results of multiple, possibly differently trained models to obtain a single result. Its one way to use an ensemble of models instead of a single one. But also for those you want to use CV in the end for choosing reasonable model.
I like your thinking. I think you have just accidentally discovered Random Forest:
https://en.wikipedia.org/wiki/Random_forest
Without repeated cv your seemingly best model is likely to be only a mediocre model when you score it on new data...
Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM's are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM's in LVQ, LVQ should be provided with a labelled training data set. But since SOM's only do clustering and not classification and thus cannot have labelled data how can SOM be used as an input for LVQ?
Does LVQ fine tune the clusters in SOM?
Before using in LVQ should SOM be put through another classification algorithm so that it can classify the inputs so that these labelled inputs maybe used in LVQ?
It must be clear that supervised differs from unsupervised because in the first the target values are known.
Therefore, the output of supervised models is a prediction.
Instead, the output of unsupervised models is a label for which we don't know the meaning yet. For this purpose, after clustering, it is necessary to do the profiling of each one of those new label.
Having said so, you could label the dataset using an unsupervised learning technique such as SOM. Then, you should profile each class in order to be sure to understand the meaning of each class.
At this point, you can pursue two different path depending on what is your final objective:
1. use this new variable as a way for dimensionality reduction
2. use this new dataset featured with the additional variable representing the class as a labelled data that you will try to predict using the LVQ
Hope this can be useful!
I trained a ensemble model (RUSBoost) for a binary classification problem by the function fitensemble() in Matlab 2014a. The training by this function is performed 10-fold cross-validation through the input parameter "kfold" of the function fitensemble().
However, the output model trained by this function cannot be used to predict the labels of new data if I use the predict(model, Xtest). I checked the Matlab documents, which says we can use kfoldPredict() function to evaluate the trained model. But I did not find any input of the new data through this function. Also, I found the structure of the trained model with cross-validation is different from that model without cross-validation. So, could anyone please advise me how to use the model, which is trained with cross-validation, to predict labels of new data? Thanks!
kfoldPredict() needs a RegressionPartitionedModel or ClassificationPartitionedEnsemble object as input. This already contains the models and data for kfold cross validation.
The RegressionPartitionedModel object has a field Trained, in which the trained learners that are used for cross validation are stored.
You can take any of these learners and use it like predict(learner, Xdata).
Edit:
If k is too large, it is possible that there is too little meaningful data in one or more iteration, so the model for that iteration is less accurate.
There are no general rules for k, but k=10 like in the MATLAB default is a good starting point to play around with it.
Maybe this is also interesting for you: https://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation
I am new to image classification, currently working on SVM(support Vector Machine) method for classifying four groups of images by multisvm function, my algorithm every time the training and testing data are randomly selected and the performance is varies at every time. Some one suggested to do cross validation i did not understand why we need cross validation and what is the main purpose of this? . My actual data set consist training matrix size 28×40000 and testing matrix size 17×40000. how to do cross validation by this data set help me. thanks in advance .
Cross validation is used to select your model. The out-of-sample error can be estimated from your validation error. As a result, you would like to select the model with the least validation error. Here the model refers to the features you want to use, and of more importance, the gamma and C in your SVM. After cross validation, you will use the selected gamma and C with the least average validation error to train the whole training data.
You may also need to estimate the performance of your features and parameters to avoid both high-bias and high-variance. Whether your model suffers underfitting or overfitting can be observed from both in-sample-error and validation error.
Ideally 10-fold is often used for cross validation.
I'm not familiar with multiSVM but you may want to check out libSVM, it is a popular, free SVM library with support for a number of different programming languages.
Here they describe cross validation briefly. It is a way to avoid over-fitting the model by breaking up the training data into sub groups. In this way you can find a model (defined by a set of parameters) which fits both sub groups optimally.
For example, in the following picture they plot the validation accuracy contours for parameterized gamma and C values which are used to define the model. From this contour plot you can tell that the heuristically optimal values (from those tested) are those that give an accuracy closer to 84 instead of 81.
Refer to this link for more detailed information on cross-validation.
You always need to cross-validate your experiments in order to guarantee a correct scientific approach. For instance, if you don't cross-validate, the results you read (such as accuracy) might be highly biased by your test set. In an extreme case, your training step might have been very weak (in terms of fitting data) and your test step might have been very good. This applies to ALL machine learning and optimization experiments, not only SVMs.
To avoid such problems just divide your initial dataset in two (for instance), then train in the first set and test in the second, and repeat the process invesely, training in the second and testing in the first. This will guarantee that any biases to the data are visible to you. As someone suggested, you can perform this with even further division: 10-fold cross-validation, means dividing your data set in 10 parts, then training in 9 and testing in 1, then repeating the process until you have tested in all parts.