It is common practice to augment data (add samples programmatically, such as random crops, etc. in the case of a dataset consisting of images) on both training and test set, or just the training data set?
Only on training. Data augmentation is used to increase the size of the training set and to get more different images.
Technically, you could use data augmentation on the test set to see how the model behaves on such images, but usually, people don't do it.
Data augmentation is done only on training set as it helps the model become more generalize and robust. So there's no point of augmenting the test set.
This answer on stats.SE makes the case for applying crops on the validation / test sets so as to make that input similar the the input in the training set that the network was trained on.
Do it only on the training set. And, of course, make sure that the augmentation does not make the label wrong (e.g. when rotating 6 and 9 by about 180°).
The reason why we use a training and a test set in the first place is that we want to estimate the error our system will have in reality. So the data for the test set should be as close to real data as possible.
If you do it on the test set, you might have the problem that you introduce errors. For example, say you want to recognize digits and you augment by rotating. Then a 6 might look like a 9. But not all examples are that easy. Better be save than sorry.
I would argue that, in some cases, using data augmentation for the validation set can be helpful.
For example, I train a lot of CNNs for medical image segmentation. Many of the augmentation transforms that I use are meant to reduce the image quality so that the network is trained to be robust against such data. If the training set looks bad and the validation set looks nice, it will be hard to compare the losses during training and therefore assessing overfit will be complicated.
I would never use augmentation for the test set unless I'm using test-time augmentation to improve results or estimate aleatoric uncertainty.
In computer vision, you can use data augmentation during test time to obtain different views on the test image. You then have to aggregate the results obtained from each image for example by averaging them.
For example, given this symbol below, changing the point of view can lead to different interpretations :
Some image preprocessing software tools like Roboflow (https://roboflow.com/) apply data augmentation to test data as well. I'd say that if one is dealing with small and rare objects, say, cerebral microbleeds (which are tiny and difficult to spot on magnetic resonance images), augmenting one's test set could be useful. Then you can verify that your model has learned to detect these objects given different orientation and brightness conditions (given that your training data has been augmented in the same way).
The goal of data augmentation is to generalize the model and make it learn more orientation of the images, such that the during testing the model is able to apprehend the test data well. So, it is well practiced to use augmentation technique only for training sets.
The point of adding validation data is to build generalized model so it is nothing but to predict real-world data. inorder to predict real-world data, the validation set should contain real data. There is no problem with augmenting validation data but it won't increase the accuracy of the model.
Here are my two cents:
You train your model on the training data and the validation data: the former to optimize your parameters, and the latter to give you an appropriate stopping condition. The test data is to give you a real-world estimate of how well you can expect your model to perform.
For training, you can augment your training data to increase robustness to various factors including, but not limited to, sampling error, bias between data sources, shifts in global data distribution, positioning, and any other sort of variation you would like to account for.
The validation data should indicate to the training method when the model is most generalizable. By this logic, if you expect to see some variation in real-world data that can be simulated using data augmentation, then by all means, the validation dataset should be augmented.
The test data, on the other hand, should not be augmented, except potentially in special scenarios where data is very limited, and an estimate of real-world performance on test data has too much variance.
You can use augmentation data in training, validation and test sets.
The only thing to avoid is using the same data from the training set in validation or test sets.
For example, if you generate 3 augmented instances from an register of the training data, make sure that no one of these 3 augmented instances accidentally ends up in the validation or test sets.
It turns out that using data from the training set, even augmented data, to validate or test a model is a methodology mistake.
I am working on this shared task http://alt.qcri.org/semeval2017/task4/index.php?id=data-and-tools
which is just a twitter sentiment analysis. Since i am pretty new to machine learning, I am not quite sure how to use both training data and testing data.
So the shared task provides two same sets of twitter tweets one without the result (train) and one with the result.
I current understandings of using these kinds of data in machine learning are as follows:
training set: we are supposed to split this into training and testing portions (90% training and 10% testing maybe?)
But the existing of a separate test data kind of confuses.
Are we supposed to use the result that we got in the test using the 10% portion of the 'training set' and compare that to the actual result 'testing set' ?
Can someone correct my understanding?
When training a machine learning model, you are feeding your algorithm with the dataset called training set, which in this stage, you are telling the algorithm what is the ground truth of each sample you put into the algorithm, that way, the algorithm learns from each sample you are feeding to it. the training set is usually 80% of the whole dataset, the other 20% of the dataset is the testing set, which in this case, you know what is the ground truth of each sample, but you let your algorithm predict what it think the truth is to each sample you let it predict. All those prediction over the testing set are based on what the algorithm have learned from the training set you fed it before.
After you make all the predictions over your testing set you can then check how accurate your model is based on the ground truth in compare to the prediction the model have made.
I am completely new to Machine Learning algorithms and I have a quick question with respect to Classification of a dataset.
Currently there is a training data that consists of two columns Message and Identifier.
Message - Typical message extracted from Log containing timestamp and some text
Identifier - Should classify the category based on the message content.
The training data was prepared by extracting a particular category from the tool and labelling it accordingly.
Now the test data contains just the message and I am trying to obtain the Category accordingly.
Which approach is most helpful in this scenario ? Is it the Supervised or Unsupervised Learning ?
I have a trained dataset and I am trying to predict the Category for the Test Data.
Thanks in advance,
Adam
If your labels are exact then you can classify using ANN, SVM etc. But labels are not exact you have to cluster data with respect to the features you have in data. K-means or nearest neighbour can be the starting point for clustering.
It is supervised learning, and a classification problem.
However, obviously you do not have the label column (the to-be-predicted value) for your testset. Thus, you cannot calculate error measures (such as False Positive Rate, Accuracy etc) for that test set.
You could, however, split the set of labeled training data that you do have into a smaller training set and a validation set. Split it 70%/30%, perhaps. Then build a prediction model from your smaller 70% training dataset. Then tune it on your 30% validation set. When accuracy is good enough, then apply it on your testset to obtain/predict the missing values.
Which techniques / algorithms to use is a different question. You do not give enough information to answer that. And even if you did you still need to tune the model yourself.
You have labels to predict, and training data.
So by definition it is a supervised problem.
Try any classifier for text, such as NB, kNN, SVM, ANN, RF, ...
It's hard to predict which will work best on your data. You willhave to try and evaluate several.
Before applying SVM on my data I want to reduce its dimension by PCA. Should I separate the Train data and Test data then apply PCA on each of them separately or apply PCA on both sets combined then separate them?
Actually both provided answers are only partially right. The crucial part here is what is the exact problem you are trying to solve. There are two basic possible settings which can be considered, and both are valid under some assumptions.
Case 1
You have some data (which you splitted to train and test) and in the future you will get more data coming from the same distribution.
If this is the case, you should fit PCA on train data, then SVM on its projection, and for testing you just apply already fitted PCA followed by already fitted SVM, and you do exactly the same for new data that will come. This way your test error (under some "size assumptions" should approximate your expected error).
Case 2
You have some data (which you splitted train and test) and in the future you will obtain a big chunk of unlabeled data and you will be able to fit your model then.
In such a case, you fit PCA on whole data provided, learn SVM on labeled part (train set) and evaluate on test set. This way, once new data arrives you can fit PCA using both your data and new ones, and then - train SVM on your old data (as this is the only one having labels). Under the assumption that again - data comes from the same distributions, everything is correct here. You use more data to fit PCA only to have a better estimator (maybe your data is really high dimensional and PCA fails with small sample?).
You should do them separately. If you run pca on both sets combined then you are going to introduce a bias in your svn. The goal of the test set is to see how your algorithm will perform without prior knowledge of the data.
Learn the Projection Matrix of PCA on the train set and use this to reduce the dimensions of the test data.
One benifit is this way you don't have to rely on collecting sufficient data in the test set if you are applying your classifier for actual run time where test data comes one sample at a time.
Also I think separate train and test PCA will fail.Why?
Think of PCA as giving you features, and then you learn a classifier over these features. If over time your data shifts, then the test features you get using PCA would be different, and you don't have a classifier trained on these features. Even if the set of directions/features of the PCA remain same but their order varies your classifier still fails.
I'm trying to perform sentiment analysis on a dataset.But there is no existing corpus that my classifier can be trained on that is similar to the dataset that I want to analyze. My question is as follows: Can I use a randomly sampled subset of this data for training/validation phases and then use the trained classifier for performing analysis on the larger dataset? I plan to introduce some variability by adding data points to the training set that are similar to the application dataset but not from that set. Is this is a valid approach?
What you are looking for is the standard procedure of cross-validation. During cross-validation you split your data on (let's assume) 80%-20% training testing data and make 5-10 (depending on the size of data you have) different splits. So I would suggest that you keep a subset of the data and then perform cross-validation on this subset. This is the optimal way to train your model.