I'm doing a self-study, learning to use deep learning for text classification.
I use the Bi-LSTM model and I tried to change parameters with 10 epochs to compare results.
The dataset contains approximately 35k rows, in total with more than 1,200k tokenized words. The dataset is labeled with 2 classes. After tuning the parameters, I did the stratified 10-fold cross validation, 8 scenarios gave normal results, but these 2 scenarios happened.
-- The fist result:
it seems like the model couldn't improve the accuracy. What might be the cause of problem?
-- the second result:
this might be worse than the first one, in this case, does it has something to do with the network’s weights update?
Correct me if I'm worng.
I'm quite new with deep learning techniques and not familiar with some technical terms, so excuse me if I used some wrong words.
Thanks in advance.
ok so you have enough data to train the model so I guess the model structure might result into something like this, try the model structure shown below and tell if this works:
input = Input layer
model = Embedding layer
model = Bidirectional layer
model = TimeDistributed
model = Flatten
model = Dense(100,activation='relu')(model)
output = Dense(3,activation='softmax')(model)
model = Model(input,output)
and then try to compile the model using Adam optimizer , and at the output use softmax activation which can I guess have some drastic impact if not used before
Related
I have trained a neural network and an XGBoost model for the same problem, now I am confused that how should I stack them. Should I just pass the output of the neural network as a parameter to the XGBoost model, or should I take the weighting of their results seperately ? Which would be better ?
This question cannot be clearly answered. I would suggest to check both possibilities and chose the one, that worked best.
Using the output of one model as input to the other model
I guess, you know, what you have to do to use the output of the NN as input to XGBoost. You should just take some time, about how you handle the test and train data (see below). Use the "probabilities" rather than the binary labels for that. Of course, you could also try it vice-versa, so that the NN gets the output of the XGBoost model as an additional input.
Using a Votingclassifier
The other possibility is to use a VotingClassifier using soft-voting. You can use VotingClassifier(voting='soft') for that (to be precise sklearn.ensemble.VotingClassifier). You could also play around with the weights here.
Difference
The big difference is, that with the first possibility the XGBoost model might learn, in what areas the NN is weak and in which it is strong, while with the VotingClassifier the outputs of both models are equally weighted for all samples and it relies on the assumption that the model output a "probability" not so close to 0 / 1 if they are not so confident about the prediciton of the specific input record. But this assumption might not be always true.
Handling of the Train/Testdata
In both cases, you need to think about, how you should handle the train/test data. The train/test data should ideally be split the same way for both models. Otherwise you might introduce some kind of data-leakage problem.
For the VotingClassifier this is no problem, because it can be used as a regular skearn model class. For the first method (output of model 1 is one feature of model 2), you should make sure, you do the train-test-split (or the cross-validation) with exactly the same records. If you don't do that, you would run the risk to validate the output of your second model on a record which was in the training set of model 1 (except for the additonal feature of course) and this clearly could cause a data-leakage problem which results in a score that appears to be better than how the model would actually perform on unseen productive data.
I have a binary classification problem I'm trying to tackle in Keras. To start, I was following the usual MNIST example, using softmax as the activation function in my output layer.
However, in my problem, the 2 classes are highly unbalanced (1 appears ~10 times more often than the other). And what's even more critical, they are non-symmetrical in the way they may be mistaken.
Mistaking an A for a B is way less severe than mistaking a B for an A. Just like a caveman trying to classify animals into pets and predators: mistaking a pet for a predator is no big deal, but the other way round will be lethal.
So my question is: how would I model something like this with Keras?
thanks a lot
A non-exhaustive list of things you could do:
Generate a balanced data set using data augmentations. If the data are images, you can add image augmentations in a custom data generator that will output balanced amounts of data from each class per batch and save the results to a new data set. If the data are tabular, you can use a library like imbalanced-learn to perform over/under sampling.
As #Daniel said you can use class_weights during training (in the fit method) in a way that mistakes on important class are penalized more. See this tutorial: Classification on imbalanced data. The same idea can be implemented with a custom loss function with/without class_weights during training.
So say for each of my ‘things’ to classify I have:
{house, flat, bungalow, electricityHeated, gasHeated, ... }
Which would be made into a feature vector:
{1,0,0,1,0,...} which would mean a house that is heated by electricity.
For my training data I would have all this data- but for the actual thing I want to classify I might only have what kind of house it is, and a couple other things- not all the data ie.
{1,0,0,?,?,...}
So how would I represent this?
I would want to find the probability that a new item would be gasHeated.
I would be using a SVM linear classifier- I don’t have any core to show because this is purely theoretical at the moment. Any help would be appreciated :)
When I read this question, it seems that you may have confused with feature and label.
You said that you want to predict whether a new item is "gasHeated", then "gasHeated" should be a label rather than a feature.
btw, one of the most-common ways to deal with missing value is to set it as "zero" (or some unused value, say -1). But normally, you should have missing value in both training data and testing data to make this trick be effective. If this only happened in your testing data but not in your training data, it means that your training data and testing data are not from the same distribution, which basically violated the basic assumption of machine learning.
Let's say you have a trained model and a testing sample {?,0,0,0}. Then you can create two new testing samples, {1,0,0,0}, {0,0,0,0}, and you will have two predictions.
I personally don't think SVM is a good approach if you have missing values in your testing dataset. Just like I have mentioned above, although you can get two new predictions, but what if each one has different predictions? It is difficult to assign a probability to results of SVM in my opinion unless you use logistic regression or Naive Bayes. I would prefer Random Forest in this situation.
I am using cross_val_score function with LeaveOneOut function as my data has 60 samples.
I am confused on how cross_val_score computes the results for each estimation in Leave One Out cross validation (LOOCV).
In the LOOCV, for one instance, it fits, let's say Decision Trees Classifier (DTC), model using 59 samples for training and predicts the single remaining one.
Then the main question is this:
Does it fit a new model at each instance (namely 60 different fits) inside cross_val_score?
If so, things get confusing.
Then I can have an average accuracy (out of 60) score for performance evaluation. But I need to come up with a best DTC model in general not just for my own data, though it is based my data.
If I use the entire data, the it fits perfectly but that model simply over-fits.
I want to have a single DTC model that works best in general based on my data.
Here is my code if that make sense:
model = DecisionTreeClassifier(random_state=27, criterion='gini', max_depth=4, max_features='auto' )
loocv = LeaveOneOut()
results = cross_val_score(model, X, y, cv=loocv)
I do not fully understand what do you want to find out.
Does it fit a new model at each instance (namely 60 different fits) inside cross_val_score?`
Yes, it does in your case. What is the follow up question to help to clarify the confusion that you have in such case?
The idea of the CV is that one gets a performance estimate of the model building procedure that you have chosen. The final model can (and should to benefit most from the data) be built on the full dataset. Then you can use it to predict on test data and you can use your cross_val_score outcome to get an estimate of performance for this model. See more elaborate answer as well as very useful links in my earlier answer.
My answer applies to a larger dataset. There might be nuisances related to small dataset treatment, that I'm not aware of, but I do not see why the logic does not generalise to this case.
For a class project, I designed a neural network to approximate sin(x), but ended up with a NN that just memorized my function over the data points I gave it. My NN took in x-values with a batch size of 200. Each x-value was multiplied by 200 different weights, mapping to 200 different neurons in my first layer. My first hidden layer contained 200 neurons, each one a linear combination of the x-values in the batch. My second hidden layer also contained 200 neurons, and my loss function was computed between the 200 neurons in my second layer and the 200 values of sin(x) that the input mapped to.
The problem is, my NN perfectly "approximated" sin(x) with 0 loss, but I know it wouldn't generalize to other data points.
What did I do wrong in designing this neural network, and how can I avoid memorization and instead design my NN's to "learn" about the patterns in my data?
It is same with any machine learning algorithm. You have a dataset based on which you try to learn "the" function f(x), which actually generated the data. In real life datasets, it is impossible to get the original function from the data, and therefore we approximate it using something g(x).
The main goal of any machine learning algorithm is to predict unseen data as best as possible using the function g(x).
Given a dataset D you can always train a model, which will perfectly classify all the datapoints (you can use a hashmap to get 0 error on the train set), but which is overfitting or memorization.
To avoid such things, you yourself have to make sure that the model does not memorise and learns the function. There are a few things which can be done. I am trying to write them down in an informal way (with links).
Train, Validation, Test
If you have large enough dataset, use Train, Validation, Test splits. Split the dataset in three parts. Typically 60%, 20% and 20% for Training, Validation and Test, respectively. (These numbers can vary based on need, also in case of imbalanced data, check how to get stratified partitions which preserve the class ratios in every split). Next, forget about the Test partition, keep it somewhere safe, don't touch it. Your model, will be trained using the Training partition. Once you have trained the model, evaluate the performance of the model using the Validation set. Then select another set of hyper-parameter configuration for your model (eg. number of hidden layer, learaning algorithm, other parameters etc.) and then train the model again, and evaluate based on Validation set. Keep on doing this for several such models. Then select the model, which got you the best validation score.
The role of validation set here is to check what the model has learned. If the model has overfit, then the validation scores will be very bad, and therefore in the above process you will discard those overfit models. But keep in mind, although you did not use the Validation set to train the model, directly, but the Validation set was used indirectly to select the model.
Once you have selected a final model based on Validation set. Now take out your Test set, as if you just got new dataset from real life, which no one has ever seen. The prediction of the model on this Test set will be an indication how well your model has "learned" as it is now trying to predict datapoints which it has never seen (directly or indirectly).
It is key to not go back and tune your model based on the Test score. This is because once you do this, the Test set will start contributing to your mode.
Crossvalidation and bootstrap sampling
On the other hand, if your dataset is small. You can use bootstrap sampling, or k-fold cross-validation. These ideas are similar. For example, for k-fold cross-validation, if k=5, then you split the dataset in 5 parts (also be carefull about stratified sampling). Let's name the parts a,b,c,d,e. Use the partitions [a,b,c,d] to train and get the prediction scores on [e] only. Next, use the partitions [a,b,c,e] and use the prediction scores on [d] only, and continue 5 times, where each time, you keep one partition alone and train the model with the other 4. After this, take an average of these scores. This is indicative of that your model might perform if it sees new data. It is also a good practice to do this multiple times and perform an average. For example, for smaller datasets, perform a 10 time 10-folds cross-validation, which will give a pretty stable score (depending on the dataset) which will be indicative of the prediction performance.
Bootstrap sampling is similar, but you need to sample the same number of datapoints (depends) with replacement from the dataset and use this sample to train. This set will have some datapoints repeated (as it was a sample with replacement). Then use the missing datapoins from the training dataset to evaluate the model. Perform this multiple times and average the performance.
Others
Other ways are to incorporate regularisation techniques in the classifier cost function itself. For example in Support Vector Machines, the cost function enforces conditions such that the decision boundary maintains a "margin" or a gap between two class regions. In neural networks one can also do similar things (although it is not same as in SVM).
In neural network you can use early stopping to stop the training. What this does, is train on the Train dataset, but at each epoch, it evaluates the performance on the Validation dataset. If the model starts to overfit from a specific epoch, then the error for Training dataset will keep on decreasing, but the error of the Validation dataset will start increasing, indicating that your model is overfitting. Based on this one can stop training.
A large dataset from real world tends not to overfit too much (citation needed). Also, if you have too many parameters in your model (to many hidden units and layers), and if the model is unnecessarily complex, it will tend to overfit. A model with lesser pameter will never overfit (though can underfit, if parameters are too low).
In the case of you sin function task, the neural net has to overfit, as it is ... the sin function. These tests can really help debug and experiment with your code.
Another important note, if you try to do a Train, Validation, Test, or k-fold crossvalidation on the data generated by the sin function dataset, then splitting it in the "usual" way will not work as in this case we are dealing with a time-series, and for those cases, one can use techniques mentioned here
First of all, I think it's a great project to approximate sin(x). It would be great if you could share the snippet or some additional details so that we could pin point the exact problem.
However, I think that the problem is that you are overfitting the data hence you are not able to generalize well to other data points.
Few tricks that might work,
Get more training points
Go for regularization
Add a test set so that you know whether you are overfitting or not.
Keep in mind that 0 loss or 100% accuracy is mostly not good on training set.