I'm looking at the SAS Viya machine learing demo. It races some machine Learning algorithms against each other on a given dataset. All models produce almost equally good "lift" as shown in lift diagrams in the output.
If you tweak the Learning to perform on a smaller subset of the data; only 0.002% of the total data set (proc partition data=&casdata partition samppct=0.002;), most algorithms get into problems producing lift.
But the neural network is still performing very well. Feature or bug? I could imagine that the script does not re-initilize the network, but it is hard to guess from the calls alone.
I got good answers over at the SAS Community posted by BrettWujek and Xinmin there:
Mats - the short answer without running some studies of my own is that neural networks are highly adaptive and can train very accurate models with far fewer observations than many other techniques. The tree-based models are going to be quite unstable with very few observations. In this case you sampled all the way down to around 20 observations...even that might be sufficient for a neural network if the space it not overly nonlinear.
As for your last comment - it seems you are referring to what is known as warm start, where a previously trained model can be used as a starting point and refined by providing new observations. That is NOT what is happening here, as that capability is only coming available in our upcoming release which is just over a month away.
Brett
And I've got some detail on this from Xinmin:
Mats, PROC NNET initializes weight random, if you specify a seed in the train statement, the initial weights are repeatable. NNET training is powered by a sophiscated nonlinear optimization solver, if the log shows "converged" status, it means the model is fit very well.
I have developed a ML model for a classification (0/1) NLP task and deployed it in production environment. The prediction of the model is displayed to users, and the users have the option to give a feedback (if the prediction was right/wrong).
How can I continuously incorporate this feedback in my model ? From a UX stand point you dont want a user to correct/teach the system more than twice/thrice for a specific input, system shld learn fast i.e. so the feedback shld be incorporated "fast". (Google priority inbox does this in a seamless way)
How does one build this "feedback loop" using which my system can improve ? I have searched a lot on net but could not find relevant material. any pointers will be of great help.
Pls dont say retrain the model from scratch by including new data points. Thats surely not how google and facebook build their smart systems
To further explain my question - think of google's spam detector or their priority inbox or their recent feature of "smart replies". Its a well known fact that they have the ability to learn / incorporate (fast) user feed.
All the while when it incorporates the user feedback fast (i.e. user has to teach the system correct output atmost 2-3 times per data point and the system start to give correct output for that data point) AND it also ensure it maintains old learnings and does not start to give wrong outputs on older data points (where it was giving right output earlier) while incorporating the learning from new data point.
I have not found any blog/literature/discussion w.r.t how to build such systems - An intelligent system that explains in detaieedback loop" in ML systems
Hope my question is little more clear now.
Update: Some related questions I found are:
Does the SVM in sklearn support incremental (online) learning?
https://datascience.stackexchange.com/questions/1073/libraries-for-online-machine-learning
http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/
https://en.wikipedia.org/wiki/Concept_drift
Update: I still dont have a concrete answer but such a recipe does exists. Read the section "Learning from the feedback" in the following blog Machine Learning != Learning Machine. In this Jean talks about "adding a feedback ingestion loop to machine". Same in here, here, here4.
There could be couple of ways to do this:
1) You can incorporate the feedback that you get from the user to only train the last layer of your model, keeping the weights of all other layers intact. Intuitively, for example, in case of CNN this means you are extracting the features using your model but slightly adjusting the classifier to account for the peculiarities of your specific user.
2) Another way could be to have a global model ( which was trained on your large training set) and a simple logistic regression which is user specific. For final predictions, you can combine the results of the two predictions. See this paper by google on how they do it for their priority inbox.
Build a simple, light model(s) that can be updated per feedback. Online Machine learning gives a number of candidates for this
Most good online classifiers are linear. In which case we can have a couple of them and achieve non-linearity by combining them via a small shallow neural net
https://stats.stackexchange.com/questions/126546/nonlinear-dynamic-online-classification-looking-for-an-algorithm
I am going to do a research project which involves predicting imminent failure of an engine using time data obtained from sensors. The data basically contains the readings of various embedded sensors every 10 minutes for many months. Such data is available for about 100 or so different units (all are the same engine model), along with the time of failure.
While I do have a reasonably good understanding of Machine Learning, I am at a loss of approaching this. I have done a few projects that involved static datasets (using SVMs, Neural Nets, Logistic Regression etc.) and even one on predicting time series. But this is quite different. While the project involves time data, it is hardly a matter of predicting the future values. Rather it is a case of anomaly detection on sequential time data.
Please could you give some ideas as to how I could approach it?
I'm particularly interested in Neural Networks/ Deep Learning, so any ideas on using them for this task would also be welcome. I would prefer to use Python or R, although I would be open to using something else if it was particularly geared for this sort of task.
Also could you give me some formal terms using which I could search for relevant literature?
Thanks
As a general comment, try hard to express everything that you know about the physical system in a model, then use that model for inference. I worked on such problems in my dissertation: Unified Prediction and Diagnosis in Engineering Systems by means of Distributed Belief Networks (see chapter 6). I can say more if you provide additional details about your problem domain.
Don't expect general machine learning models (neural networks, SVM, etc) to figure out the structure of the problem for you. Having the right form of the model is much, much more important than having a general model + lots of data -- this is the summary of my experience.
After running the Machine Learner Algorithm (SVM) on training data using GATE tool, I would like to test it on testing data. My question is, should I use the same trained data to be tested, also, how could the model extract the entities from the test data while the test data not annotated with the annotations that have been learnt in the trained data.
I followed the tutorial on this link http://gate.ac.uk/sale/talks/gate-course-may11/track-3/module-11-machine-learning/module-11.pdf but at the end it was a bit confusing when it talks about splitting the dataset into training and testing.
In GATE you have 3 modes of the machine learning PR - for training, evaluation and application.
What happens when you train is that the ML PR is checking the selected annotation (let's say Token), collecting it's features and learning the target class (i.e. Person, Mention or whatever). Using the example docs, the ML PR creates a model which holds values for features and basically "learns" how to classify new Tokens (or sentences, or other).
When testing, you provide the ML PR only the Tokens with all their features. Then the ML PR uses them as input for its model and decides if or what Mention to create. The ML PR actually needs everything that was there in the training corpus, except the label / target class / mention - the decision that should be made.
I think the GATE ML PR ignores the labels when in test mode, so it's not crucial to remove it.
Evaluation is a helpful option, where training and testing are done automatically, the corpus is split and results are presented. What it does is split the corpus in 2, train on one part, apply the model on the other, compare the gold standard to what it labeled. Repeat with different splits.
The usual sequence is to train and evaluate, check results, fix, add features, etc. and when you're happy with the evaluation results, switch to application and run on data that doesn't have labels.
It is crucial that you run the same pre-processing when you're training and testing. For instance if in training you've run a POS tagger and you skip this when testing, the ML PR won't have the "Token.category" feature and will calculate very different results.
Now to your questions :)
NO! Don't use the same data for testing, that is a very common mistake, if you get suspiciously good results, first check if you're doing that.
In the tutorial, when you split the corpus both parts will have all the annotations as before, so the ML PR will have all the features it needed. In real life, you'll have to run some pre-processing first as docs will come without tokens or anything.
Splitting in their case is done very simple - just save all docs to files, split files in two folders, load them as two corpora.
Hope this helps :)
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What is machine learning ?
What does machine learning code do ?
When we say that the machine learns, does it modify the code of itself or it modifies history (database) which will contain the experience of code for given set of inputs?
What is a machine learning ?
Essentially, it is a method of teaching computers to make and improve predictions or behaviors based on some data. What is this "data"? Well, that depends entirely on the problem. It could be readings from a robot's sensors as it learns to walk, or the correct output of a program for certain input.
Another way to think about machine learning is that it is "pattern recognition" - the act of teaching a program to react to or recognize patterns.
What does machine learning code do ?
Depends on the type of machine learning you're talking about. Machine learning is a huge field, with hundreds of different algorithms for solving myriad different problems - see Wikipedia for more information; specifically, look under Algorithm Types.
When we say machine learns, does it modify the code of itself or it modifies history (Data Base) which will contain the experience of code for given set of inputs ?
Once again, it depends.
One example of code actually being modified is Genetic Programming, where you essentially evolve a program to complete a task (of course, the program doesn't modify itself - but it does modify another computer program).
Neural networks, on the other hand, modify their parameters automatically in response to prepared stimuli and expected response. This allows them to produce many behaviors (theoretically, they can produce any behavior because they can approximate any function to an arbitrary precision, given enough time).
I should note that your use of the term "database" implies that machine learning algorithms work by "remembering" information, events, or experiences. This is not necessarily (or even often!) the case.
Neural networks, which I already mentioned, only keep the current "state" of the approximation, which is updated as learning occurs. Rather than remembering what happened and how to react to it, neural networks build a sort of "model" of their "world." The model tells them how to react to certain inputs, even if the inputs are something that it has never seen before.
This last ability - the ability to react to inputs that have never been seen before - is one of the core tenets of many machine learning algorithms. Imagine trying to teach a computer driver to navigate highways in traffic. Using your "database" metaphor, you would have to teach the computer exactly what to do in millions of possible situations. An effective machine learning algorithm would (hopefully!) be able to learn similarities between different states and react to them similarly.
The similarities between states can be anything - even things we might think of as "mundane" can really trip up a computer! For example, let's say that the computer driver learned that when a car in front of it slowed down, it had to slow down to. For a human, replacing the car with a motorcycle doesn't change anything - we recognize that the motorcycle is also a vehicle. For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would "learn" from the car example and be able to generalize to the motorcycle example automatically.
Machine learning is a field of computer science, probability theory, and optimization theory which allows complex tasks to be solved for which a logical/procedural approach would not be possible or feasible.
There are several different categories of machine learning, including (but not limited to):
Supervised learning
Reinforcement learning
Supervised Learning
In supervised learning, you have some really complex function (mapping) from inputs to outputs, you have lots of examples of input/output pairs, but you don't know what that complicated function is. A supervised learning algorithm makes it possible, given a large data set of input/output pairs, to predict the output value for some new input value that you may not have seen before. The basic method is that you break the data set down into a training set and a test set. You have some model with an associated error function which you try to minimize over the training set, and then you make sure that your solution works on the test set. Once you have repeated this with different machine learning algorithms and/or parameters until the model performs reasonably well on the test set, then you can attempt to use the result on new inputs. Note that in this case, the program does not change, only the model (data) is changed. Although one could, theoretically, output a different program, but that is not done in practice, as far as I am aware. An example of supervised learning would be the digit recognition system used by the post office, where it maps the pixels to labels in the set 0...9, using a large set of pictures of digits that were labeled by hand as being in 0...9.
Reinforcement Learning
In reinforcement learning, the program is responsible for making decisions, and it periodically receives some sort of award/utility for its actions. However, unlike in the supervised learning case, the results are not immediate; the algorithm could prescribe a large sequence of actions and only receive feedback at the very end. In reinforcement learning, the goal is to build up a good model such that the algorithm will generate the sequence of decisions that lead to the highest long term utility/reward. A good example of reinforcement learning is teaching a robot how to navigate by giving a negative penalty whenever its bump sensor detects that it has bumped into an object. If coded correctly, it is possible for the robot to eventually correlate its range finder sensor data with its bumper sensor data and the directions that sends to the wheels, and ultimately choose a form of navigation that results in it not bumping into objects.
More Info
If you are interested in learning more, I strongly recommend that you read Pattern Recognition and Machine Learning by Christopher M. Bishop or take a machine learning course. You may also be interested in reading, for free, the lecture notes from CIS 520: Machine Learning at Penn.
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Read more on Wikipedia
Machine learning code records "facts" or approximations in some sort of storage, and with the algorithms calculates different probabilities.
The code itself will not be modified when a machine learns, only the database of what "it knows".
Machine learning is a methodology to create a model based on sample data and use the model to make a prediction or strategy. It belongs to artificial intelligence.
Machine learning is simply a generic term to define a variety of learning algorithms that produce a quasi learning from examples (unlabeled/labeled). The actual accuracy/error is entirely determined by the quality of training/test data you provide to your learning algorithm. This can be measured using a convergence rate. The reason you provide examples is because you want the learning algorithm of your choice to be able to informatively by guidance make generalization. The algorithms can be classed into two main areas supervised learning(classification) and unsupervised learning(clustering) techniques. It is extremely important that you make an informed decision on how you plan on separating your training and test data sets as well as the quality that you provide to your learning algorithm. When you providing data sets you want to also be aware of things like over fitting and maintaining a sense of healthy bias in your examples. The algorithm then basically learns wrote to wrote on the basis of generalization it achieves from the data you have provided to it both for training and then for testing in process you try to get your learning algorithm to produce new examples on basis of your targeted training. In clustering there is very little informative guidance the algorithm basically tries to produce through measures of patterns between data to build related sets of clusters e.g kmeans/knearest neighbor.
some good books:
Introduction to ML (Nilsson/Stanford),
Gaussian Process for ML,
Introduction to ML (Alpaydin),
Information Theory Inference and Learning Algorithms (very useful book),
Machine Learning (Mitchell),
Pattern Recognition and Machine Learning (standard ML course book at Edinburgh and various Unis but relatively a heavy reading with math),
Data Mining and Practical Machine Learning with Weka (work through the theory using weka and practice in Java)
Reinforcement Learning there is a free book online you can read:
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
IR, IE, Recommenders, and Text/Data/Web Mining in general use alot of Machine Learning principles. You can even apply Metaheuristic/Global Optimization Techniques here to further automate your learning processes. e.g apply an evolutionary technique like GA (genetic algorithm) to optimize your neural network based approach (which may use some learning algorithm). You can approach it purely in form of a probablistic machine learning approach for example bayesian learning. Most of these algorithms all have a very heavy use of statistics. Concepts of convergence and generalization are important to many of these learning algorithms.
Machine learning is the study in computing science of making algorithms that are able to classify information they haven't seen before, by learning patterns from training on similar information. There are all sorts of kinds of "learners" in this sense. Neural networks, Bayesian networks, decision trees, k-clustering algorithms, hidden markov models and support vector machines are examples.
Based on the learner, they each learn in different ways. Some learners produce human-understandable frameworks (e.g. decision trees), and some are generally inscrutable (e.g. neural networks).
Learners are all essentially data-driven, meaning they save their state as data to be reused later. They aren't self-modifying as such, at least in general.
I think one of the coolest definitions of machine learning that I've read is from this book by Tom Mitchell. Easy to remember and intuitive.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E
Shamelessly ripped from Wikipedia: Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
Quite simply, machine learning code accomplishes a machine learning task. That can be a number of things from interpreting sensor data to a genetic algorithm.
I would say it depends. No, modifying code is not normal, but is not outside the realm of possibility. I would also not say that machine learning always modifies a history. Sometimes we have no history to build off of. Sometime we simply want to react to the environment, but not actually learn from our past experiences.
Basically, machine learning is a very wide-open discipline that contains many methods and algorithms that make it impossible for there to be 1 answer to your 3rd question.
Machine learning is a term that is taken from the real world of a person, and applied on something that can't actually learn - a machine.
To add to the other answers - machine learning will not (usually) change the code, but it might change it's execution path and decision based on previous data or new gathered data and hence the "learning" effect.
there are many ways to "teach" a machine - you give weights to many parameter of an algorithm, and then have the machine solve it for many cases, each time you give her a feedback about the answer and the machine adjusts the weights according to how close the machine answer was to your answer or according to the score you gave it's answer, or according to some results test algorithm.
This is one way of learning and there are many more...