So this question may seem a little stupid but I couldn't wrap my head around it.
What is the purpose of test data? Is it only to calculate accuracy of the classifier? I'm using Naive Bayes for sentiment analysis of tweets. Once I train my classifier using training data, I use test data just to calculate accuracy of the classifier. How can I use the test data to improve classifier's performance?
In doing general supervised machine learning, the test data set plays a critical role in determining how well your model is performing. You typically will build a model with say 90% of your input data, leaving 10% aside for testing. You then check the accuracy of that model by seeing how well it does against the 10% training set. The performance of the model against the test data is meaningful because the model has never "seen" this data. If the model be statistically valid, then it should perform well on both the training and test data sets. This general procedure is called cross validation and you can read more about it here.
You don't -- like you surmise, the test data is used for testing, and mustn't be used for anything else, lest you skew your accuracy measurements. This is an important cornerstone of any machine learning -- you only fool yourself if you use your test data for training.
If you are considering desperate measures like that, the proper way forward is usually to re-examine your problem space and the solution you have. Does it adequately model the problem you are trying to solve? If not, can you devise a better model which captures the essence of the problem?
Machine learning is not a silver bullet. It will not solve your problem for you. Too many failed experiments prove over and over again, "garbage in -- garbage out".
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I was asked in an interview to solve a use case with the help of machine learning. I have to use a Machine Learning algorithm to identify fraud from transactions. My training dataset has lets say 100,200 transactions, out of which 100,000 are legal transactions and 200 are fraud.
I cannot use the dataset as a whole to make the model because it would be a biased dataset and the model would be a very bad one.
Lets say for example I take a sample of 200 good transactions which represent the dataset well(good transactions), and the 200 fraud ones and make the model using this as the training data.
The question I was asked was that how would I scale up the 200 good transactions to the whole data set of 100,000 good records so that my result can be mapped to all types of transactions. I have never solved this kind of a scenario so I did not know how to approach it.
Any kind of guidance as to how I can go about it would be helpful.
This is a general question thrown in an interview. Information about the problem is succinct and vague (we don't know for example the number of features!). First thing you need to ask yourself is What do the interviewer wants me to respond? So, based on this context the answer has to be formulated in a similar general way. This means that we don't have to find 'the solution' but instead give arguments that show that we actually know how to approach the problem instead of solving it.
The problem we have presented with is that the minority class (fraud) is only a ~0.2% of the total. This is obviously a huge imbalance. A predictor that only predicted all cases as 'non fraud' would get a classification accuracy of 99.8%! Therefore, definitely something has to be done.
We will define our main task as a binary classification problem where we want to predict whether a transaction is labelled as positive (fraud) or negative (not fraud).
The first step would be considering what techniques we do have available to reduce imbalance. This can be done either by reducing the majority class (undersampling) or increasing the number of minority samples (oversampling). Both have drawbacks though. The first implies a severe loss of potential useful information from the dataset, while the second can present problems of overfitting. Some techniques to improve overfitting are SMOTE and ADASYN, which use strategies to improve variety in the generation of new synthetic samples.
Of course, cross-validation in this case becomes paramount. Additionally, in case we are finally doing oversampling, this has to be 'coordinated' with the cross-validation approach to ensure we are making the most of these two ideas. Check http://www.marcoaltini.com/blog/dealing-with-imbalanced-data-undersampling-oversampling-and-proper-cross-validation for more details.
Apart from these sampling ideas, when selecting our learner, many ML methods can be trained/optimised for specific metrics. In our case, we do not want to optimise accuracy definitely. Instead, we want to train the model to optimise either ROC-AUC or specifically looking for a high recall even at a loss of precission, as we want to predict all the positive 'frauds' or at least raise an alarm even though some will prove false alarms. Models can adapt internal parameters (thresholds) to find the optimal balance between these two metrics. Have a look at this nice blog for more info about metrics: https://www.analyticsvidhya.com/blog/2016/02/7-important-model-evaluation-error-metrics/
Finally, is only a matter of evaluate the model empirically to check what options and parameters are the most suitable given the dataset. Following these ideas does not guarantee 100% that we are going to be able to tackle the problem at hand. But it ensures we are in a much better position to try to learn from data and being able to get rid of those evil fraudsters out there, while perhaps getting a nice job along the way ;)
In this problem you want to classify transactions as good or fraud. However your data is really imbalance. In that you will probably be interested by Anomaly detection. I will let you read all the article for more details but I will quote a few parts in my answer.
I think this will convince you that this is what you are looking for to solve this problem:
Is it not just Classification?
The answer is yes if the following three conditions are met.
You have labeled training data Anomalous and normal classes are
balanced ( say at least 1:5) Data is not autocorrelated. ( That one
data point does not depend on earlier data points. This often breaks
in time series data). If all of above is true, we do not need an
anomaly detection techniques and we can use an algorithm like Random
Forests or Support Vector Machines (SVM).
However, often it is very hard to find training data, and even when
you can find them, most anomalies are 1:1000 to 1:10^6 events where
classes are not balanced.
Now to answer your question:
Generally, the class imbalance is solved using an ensemble built by
resampling data many times. The idea is to first create new datasets
by taking all anomalous data points and adding a subset of normal data
points (e.g. as 4 times as anomalous data points). Then a classifier
is built for each data set using SVM or Random Forest, and those
classifiers are combined using ensemble learning. This approach has
worked well and produced very good results.
If the data points are autocorrelated with each other, then simple
classifiers would not work well. We handle those use cases using time
series classification techniques or Recurrent Neural networks.
I would also suggest another approach of the problem. In this article the author said:
If you do not have training data, still it is possible to do anomaly
detection using unsupervised learning and semi-supervised learning.
However, after building the model, you will have no idea how well it
is doing as you have nothing to test it against. Hence, the results of
those methods need to be tested in the field before placing them in
the critical path.
However you do have a few fraud data to test if your unsupervised algorithm is doing well or not, and if it is doing a good enough job, it can be a first solution that will help gathering more data to train a supervised classifier later.
Note that I am not an expert and this is just what I've come up with after mixing my knowledge and some articles I read recently on the subject.
For more question about machine learning I suggest you to use this stackexchange community
I hope it will help you :)
I have used the extreme learning machine for classification purpose and found that my classification accuracy is only at 70+% which leads me to use the ensemble method by creating more classification model and testing data will be classified based on the majority of the models' classification. However, this method only increase classification accuracy by a small margin. Can I asked what are the other methods which can be used to improve classification accuracy of the 2 dimension linearly inseparable dataset ?
Your question is very broad ... There's no way to help you properly without knowing the real problem you are treating. But, some methods to enhance a classification accuracy, talking generally, are:
1 - Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution. Then you will know what data is better to train a more accurate model.
2 - Cross Dataset : The same as cross validation, but using different datasets.
3 - Tuning your model : Its basically change the parameters you're using to train your classification model (IDK which classification algorithm you're using so its hard to help more).
4 - Improve, or use (if you're not using) the normalization process : Discover which techniques (change the geometry, colors etc) will provide a more concise data to you to use on the training.
5 - Understand more the problem you're treating... Try to implement other methods to solve the same problem. Always there's at least more than one way to solve the same problem. You maybe not using the best approach.
Enhancing a model performance can be challenging at times. I’m sure, a lot of you would agree with me if you’ve found yourself stuck in a similar situation. You try all the strategies and algorithms that you’ve learnt. Yet, you fail at improving the accuracy of your model. You feel helpless and stuck. And, this is where 90% of the data scientists give up. Let’s dig deeper now. Now we’ll check out the proven way to improve the accuracy of a model:
Add more data
Treat missing and Outlier values
Feature Engineering
Feature Selection
Multiple algorithms
Algorithm Tuning
Ensemble methods
Cross Validation
if you feel the information is lacking then this link should you learn, hopefully can help : https://www.analyticsvidhya.com/blog/2015/12/improve-machine-learning-results/
sorry if the information I give is less satisfactory
We have a dataset with 10,000 manually labeled instances, and a classifier that was trained on all of this data.
The classifier was then evaluated on ALL of this data to obtain a 95% success rate.
What exactly is wrong with this approach? Is it just that the statistic 95% is not very informative in this setup? Can there still be some value in this 95% number? While I understand that, theoretically, it is not a good idea, I don't have enough experience in this area to be sure by myself. Also note that I have neither built nor evaluated the classifier in question.
Common sense aside, could someone give me a very solid, authoritative reference, saying that this setup is somehow wrong?
For example, this page does say
Evaluating model performance with the data used for training is not acceptable in data mining because it can easily generate overoptimistic and overfitted models.
However, this is hardly an authoritative reference. In fact, this quote is plainly wrong, as the evaluation has nothing to do with generating overfitted models. It could generate overoptimistic data scientists who would choose the wrong model, but a particular evaluation strategy does not have anything to do with overfitting models per se.
The problem is the possibility of overfitting. That does not mean that there is no value in the accuracy you reported for that entire data set, as it can be considered an estimate of the upper bound for the performance of the classifier on new data.
It is subjective to say who constitutes a "very solid, authoritative reference"; however Machine Learning by Tom Mitchell (ISBN 978-0070428072) is a widely read and oft-cited text that discusses the problem of overfitting in general and specifically with regard to decision trees and artificial neural networks. In addition to discussion of overfitting, the text also discusses various approaches to the training and validation set approach (e.g., cross-validation).
For example: If I want to train a classifier (maybe SVM), how many sample do I need to collect? Is there a measure method for this?
It is not easy to know how many samples you need to collect. However you can follow these steps:
For solving a typical ML problem:
Build a dataset a with a few samples, how many? it will depend on the kind of problem you have, don't spend a lot of time now.
Split your dataset into train, cross, test and build your model.
Now that you've built the ML model, you need to evaluate how good it is. Calculate your test error
If your test error is beneath your expectation, collect new data and repeat steps 1-3 until you hit a test error rate you are comfortable with.
This method will work if your model is not suffering "high bias".
This video from Coursera's Machine Learning course, explains it.
Unfortunately, there is no simple method for this.
The rule of thumb is the bigger, the better, but in practical use, you have to gather the sufficient amount of data. By sufficient I mean covering as big part of modeled space as you consider acceptable.
Also, amount is not everything. The quality of test samples is very important too, i.e. training samples should not contain duplicates.
Personally, when I don't have all possible training data at once, I gather some training data and then train a classifier. Then I classifier quality is not acceptable, I gather more data, etc.
Here is some piece of science about estimating training set quality.
This depends a lot on the nature of the data and the prediction you are trying to make, but as a simple rule to start with, your training data should be roughly 10X the number of your model parameters. For instance, while training a logistic regression with N features, try to start with 10N training instances.
For an empirical derivation of the "rule of 10", see
https://medium.com/#malay.haldar/how-much-training-data-do-you-need-da8ec091e956
I am trying to solve some classification problem. It seems many classical approaches follow a similar paradigm. That is, train a model with some training set and than use it to predict the class labels for new instances.
I am wondering if it is possible to introduce some feedback mechanism into the paradigm. In control theory, introducing a feedback loop is an effective way to improve system performance.
Currently a straight forward approach on my mind is, first we start with a initial set of instances and train a model with them. Then each time the model makes a wrong prediction, we add the wrong instance into the training set. This is different from blindly enlarge the training set because it is more targeting. This can be seen as some kind of negative feedback in the language of control theory.
Is there any research going on with the feedback approach? Could anyone shed some light?
There are two areas of research that spring to mind.
The first is Reinforcement Learning. This is an online learning paradigm that allows you to get feedback and update your policy (in this instance, your classifier) as you observe the results.
The second is active learning, where the classifier gets to select examples from a pool of unclassified examples to get labelled. The key is to have the classifier choose the examples for labelling which best improve its accuracy by choosing difficult examples under the current classifier hypothesis.
I have used such feedback for every machine-learning project I worked on. It allows to train on less data (thus training is faster) than by selecting data randomly. The model accuracy is also improved faster than by using randomly selected training data. I'm working on image processing (computer vision) data so one other type of selection I'm doing is to add clustered false (wrong) data instead of adding every single false data. This is because I assume I will always have some fails, so my definition for positive data is when it is clustered in the same area of the image.
I saw this paper some time ago, which seems to be what you are looking for.
They are basically modeling classification problems as Markov decision processes and solving using the ACLA algorithm. The paper is much more detailed than what I could write here, but ultimately they are getting results that outperform the multilayer perceptron, so this looks like a pretty efficient method.