I am building a sentiment analysis program using some tweets i have gathered. The labeled data which i have gathered would go through a neural network which classifies them into two classes, positive and negative.
The data is still being labeled. So far i have observed that the positive category has very small number of observations.
The records for positive category in my training set could be about 5% of the training data set (the same ratio could reflect in the population as well).
Would this create problems in the final "program" ?
Size of the data set is about 5000 records.
Yes, yes it can. There are two things to consider:
5% of 5000 is 250. Thus you will try to model data distribution of your class based on just 250 samples. This might be orders of magnitude to small for neural network. Consequently you might need 40x more data to have a representative sample of your data. While you can easily reduce the majority class through subsampling, without the big risk of destroing the structure - there is no way to get "more structure" from less points (you can replicate points, add noise etc. but this does not add structure, this just adds assumptions).
Class imbalance can also lead to convergence to naive solutions, like "always False" which has 95% accuracy. Here you can simply play around with the cost function to make it more robust to imbalance (in particular - train splits suggested by #PureW is nothing else like "black box" method of trying to change the loss function so it has bigger weight on minority class. When you have access to your classifier loss, like in NN you should not due this - but instead change the cost function and still keep all the data).
Without even splits of the different classes, you may want to introduce weights in your loss function such that errors in the smaller class are deemed more important.
Another solution, since 5000 samples may or may not be much data depending on your problem, could be to sample more datasets. You basically take this set of 5000 samples, and sample datapoints from it such that you have a new dataset with even split of the classes. This means the new dataset is only 10% of your original dataset. But it is evenly split between the classes. You can redo this sampling many times and end up with several datasets, useful in bootstrap aggregating.
Related
I am dealing with a repeating pattern in time series data. My goal is to classify every pattern as 1, and anything that does not follow the pattern as 0. The pattern repeats itself between every two peaks as shown below in the image.
The patterns are not necessarily fixed in sample size but stay within approximate sample size, let's say 500samples +-10%. The heights of the peaks can change. The random signal (I called it random, but basically it means not following pattern shape) can also change in value.
The data is from a sensor. Patterns are when the device is working smoothly. If the device is malfunctioning, then I will not see the patterns and will get something similar to the class 0 I have shown in the image.
What I have done so far is building a logistic regression model. Here are my steps for data preparation:
Grab data between every two consecutive peaks, resample it to a fixed size of 100 samples, scale data to [0-1]. This is class 1.
Repeated step 1 on data between valley and called it class 0.
I generated some noise, and repeated step 1 on chunk of 500 samples to build extra class 0 data.
Bottom figure shows my predictions on the test dataset. Prediction on the noise chunk is not great. I am worried in the real data I may get even more false positives. Any idea on how I can improve my predictions? Any better approach when there is no class 0 data available?
I have seen similar question here. My understanding of Hidden Markov Model is limited but I believe it's used to predict future data. My goal is to classify a sliding window of 500 sample throughout my data.
I have some proposals, that you could try out.
First, I think in this field often recurrent neural networks are used (e.g. LSTMs). But I also heard that some people also work with tree based method like light gbm (I think Aileen Nielsen uses this approach).
So if you don't want to dive into neural networks, which is probably not necessary, because your signals seem to be distinguishable relative easily, you can give light gbm (or other tree ensamble methods) a chance.
If you know the maximum length of a positive sample, you can define the length of your "sliding sample-window" that becomes your input vector (so each sample in the sliding window becomes one input feature), then I would add an extra attribute with the number of samples when the last peak occured (outside/before the sample window). Then you can check in how many steps you let your window slide over the data. This also depends on the memory you have available for this.
But maybe it would be wise then to skip some of the windows between a change between positive and negative, because the states might not be classifiable unambiguously.
In case memory becomes an issue, neural networks could be the better choice, because for training they do not need all training data available at once, so you can generate your input data in batches. With tree based methods this possible does not exist or only in a very limited way.
I'm not sure of what you are trying to achieve.
If you want to characterize what is a peak or not - which is an after the facts classification - then you can use a simple rule to define peaks such as signal(t) - average(signal, t-N to t) > T, with T a certain threshold and N a number of data points to look backwards to.
This would qualify what is a peak (class 1) and what is not (class 0), hence does a classification of patterns.
If your goal is to predict that a peak is going to happen few time units before the peak (on time t), using say data from t-n1 to t-n2 as features, then logistic regression might not necessarily be the best choice.
To find the right model you have to start with visualizing the features you have from t-n1 to t-n2 for every peak(t) and see if there is any pattern you can find. And it can be anything:
was there a peak in in the n3 days before t ?
is there a trend ?
was there an outlier (transform your data into exponential)
in order to compare these patterns, think of normalizing them so that the n2-n1 data points go from 0 to 1 for example.
If you find a pattern visually then you will know what kind of model is likely to work, on which features.
If you don't then it's likely that the white noise you added will be as good. so you might not find a good prediction model.
However, your bottom graph is not so bad; you have only 2 major false positives out of >15 predictions. This hints at better feature engineering.
I am reading the a deep learning with python book.
After reading chapter 4, Fighting Overfitting, I have two questions.
Why might increasing the number of epochs cause overfitting?
I know increasing increasing the number of epochs will involve more attempts at gradient descent, will this cause overfitting?
During the process of fighting overfitting, will the accuracy be reduced ?
I'm not sure which book you are reading, so some background information may help before I answer the questions specifically.
Firstly, increasing the number of epochs won't necessarily cause overfitting, but it certainly can do. If the learning rate and model parameters are small, it may take many epochs to cause measurable overfitting. That said, it is common for more training to do so.
To keep the question in perspective, it's important to remember that we most commonly use neural networks to build models we can use for prediction (e.g. predicting whether an image contains a particular object or what the value of a variable will be in the next time step).
We build the model by iteratively adjusting weights and biases so that the network can act as a function to translate between input data and predicted outputs. We turn to such models for a number of reasons, often because we just don't know what the function is/should be or the function is too complex to develop analytically. In order for the network to be able to model such complex functions, it must be capable of being highly-complex itself. Whilst this complexity is powerful, it is dangerous! The model can become so complex that it can effectively remember the training data very precisely but then fail to act as an effective, general function that works for data outside of the training set. I.e. it can overfit.
You can think of it as being a bit like someone (the model) who learns to bake by only baking fruit cake (training data) over and over again – soon they'll be able to bake an excellent fruit cake without using a recipe (training), but they probably won't be able to bake a sponge cake (unseen data) very well.
Back to neural networks! Because the risk of overfitting is high with a neural network there are many tools and tricks available to the deep learning engineer to prevent overfitting, such as the use of dropout. These tools and tricks are collectively known as 'regularisation'.
This is why we use development and training strategies involving test datasets – we pretend that the test data is unseen and monitor it during training. You can see an example of this in the plot below (image credit). After about 50 epochs the test error begins to increase as the model has started to 'memorise the training set', despite the training error remaining at its minimum value (often training error will continue to improve).
So, to answer your questions:
Allowing the model to continue training (i.e. more epochs) increases the risk of the weights and biases being tuned to such an extent that the model performs poorly on unseen (or test/validation) data. The model is now just 'memorising the training set'.
Continued epochs may well increase training accuracy, but this doesn't necessarily mean the model's predictions from new data will be accurate – often it actually gets worse. To prevent this, we use a test data set and monitor the test accuracy during training. This allows us to make a more informed decision on whether the model is becoming more accurate for unseen data.
We can use a technique called early stopping, whereby we stop training the model once test accuracy has stopped improving after a small number of epochs. Early stopping can be thought of as another regularisation technique.
More attempts of decent(large number of epochs) can take you very close to the global minima of the loss function ideally, Now since we don't know anything about the test data, fitting the model so precisely to predict the class labels of the train data may cause the model to lose it generalization capabilities(error over unseen data). In a way, no doubt we want to learn the input-output relationship from the train data, but we must not forget that the end goal is for the model to perform well over the unseen data. So, it is a good idea to stay close but not very close to the global minima.
But still, we can ask what if I reach the global minima, what can be the problem with that, why would it cause the model to perform badly on unseen data?
The answer to this can be that in order to reach the global minima we would be trying to fit the maximum amount of train data, this will result in a very complex model(since it is less probable to have a simpler spatial distribution of the selected number of train data that is fortunately available with us). But what we can assume is that a large amount of unseen data(say for facial recognition) will have a simpler spatial distribution and will need a simpler Model for better classification(I mean the entire world of unseen data, will definitely have a pattern that we can't observe just because we have an access small fraction of it in the form of training data)
If you incrementally observe points from a distribution(say 50,100,500, 1000 ...), we will definitely find the structure of the data complex until we have observed a sufficiently large number of points (max: the entire distribution), but once we have observed enough points we can expect to observe the simpler pattern present in the data that can be easily classified.
In short, a small fraction of train data should have a complex structure as compared to the entire dataset. And overfitting to the train data may cause our model to perform worse on the test data.
One analogous example to emphasize the above phenomenon from day to day life is as follows:-
Say we meet N number of people till date in our lifetime, while meeting them we naturally learn from them(we become what we are surrounded with). Now if we are heavily influenced by each individual and try to tune to the behaviour of all the people very closely, we develop a personality that closely resembles the people we have met but on the other hand we start judging every individual who is unlike me -> unlike the people we have already met. Becoming judgemental takes a toll on our capability to tune in with new groups since we trained very hard to minimize the differences with the people we have already met(the training data). This according to me is an excellent example of overfitting and loss in genralazition capabilities.
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.
I am experimenting with classification using neural networks (I am using tensorflow).
And unfortunately the training of my neural network gets stuck at 42% accuracy.
I have 4 classes, into which I try to classify the data.
And unfortunately, my data set is not well balanced, meaning that:
43% of the data belongs to class 1 (and yes, my network gets stuck predicting only this)
37% to class 2
13% to class 3
7% to class 4
The optimizer I am using is AdamOptimizer and the cost function is tf.nn.softmax_cross_entropy_with_logits.
I was wondering if the reason for my training getting stuck at 42% is really the fact that my data set is not well balanced, or because the nature of the data is really random, and there are really no patterns to be found.
Currently my NN consists of:
input layer
2 convolution layers
7 fully connected layers
output layer
I tried changing this structure of the network, but the result is always the same.
I also tried Support Vector Classification, and the result is pretty much the same, with small variations.
Did somebody else encounter similar problems?
Could anybody please provide me some hints how to get out of this issue?
Thanks,
Gerald
I will assume that you have already double, triple and quadruple checked that the data going in is matching what you expect.
The question is quite open-ended, and even a topic for research. But there are some things that can help.
In terms of better training, there's two normal ways in which people train neural networks with an unbalanced dataset.
Oversample the examples with lower frequency, such that the proportion of examples for each class that the network sees is equal. e.g. in every batch, enforce that 1/4 of the examples are from class 1, 1/4 from class 2, etc.
Weight the error for misclassifying each class by it's proportion. e.g. incorrectly classifying an example of class 1 is worth 100/43, while incorrectly classifying an example of class 4 is worth 100/7
That being said, if your learning rate is good, neural networks will often eventually (after many hours of just sitting there) jump out of only predicting for one class, but they still rarely end well with a badly skewed dataset.
If you want to know whether or not there are patterns in your data which can be determined, there is a simple way to do that.
Create a new dataset by randomly select elements from all of your classes such that you have an even number of all of them (i.e. if there's 700 examples of class 4, then construct a dataset by randomly selecting 700 examples from every class)
Then you can use all of your techniques on this new dataset.
Although, this paper suggests that even with random labels, it should be able to find some pattern that it understands.
Firstly you should check if your model is overfitting or underfitting, both of which could cause low accuracy. Check the accuracy of both training set and dev set, if accuracy on training set is much higher than dev/test set, the model may be overfiiting, and if accuracy on training set is as low as it on dev/test set, then it could be underfitting.
As for overfiiting, more data or simpler learning structures may work while make your structure more complex and longer training time may solve underfitting problem
I'm new to data mining and I'm trying to train decision tree, but the data set I've chosen is very biased therefore the result that I am getting is also biased. I've searched online and I came across with balanced accuracy. I'm not satisfied with the result.
Will it be a good idea if I sample my data set in such a way that I proportion it equally, as in 1000 cases of YES and 1000 of NO?
One way to handle class imbalance is to undersample the larger class so that the class distribution is approximately half and half.
Answer of your question is yes, provided 1000 is the size of smaller class so that you lose less larger-class data points.
Note: While selecting from larger-class data points, try to leave out those data points which have more missing values.
You can also give weightage while modelling. you can assign higher weight to minority class it will compensate the imbalance.