My goal of a project is to correctly assign medications. I have a large catalog at my disposal for this purpose. However, the medications do not appear there in exactly the same spelling. Possibly additional information was added or possible parts of the prescription were abbreviated.
I was already able to implement a possible algorithm using the Levensthein distance (token_set_ratio).
Because of the sometimes long additional information this algorithm assigns wrong medications, I wanted to ask if there are better algorithms for comparing strings. For example, does it make sense to implement machine learning algorithms or NLP technology? This is a relatively new area for me. I would appreciate any ideas or inspiration.
This sounds like a classic Deduplication task. For example, have a look at dedupe. This tool lets you annotate training examples and learns when two items refer to the same thing. It can be used with as few as 10 training sanples and has an active learning approach implemented.
I used machine learning to train depression related sentences. And it was LinearSVC that performed best. In addition to LinearSVC, I experimented with MultinomialNB and LogisticRegression, and I chose the model with the highest accuracy among the three. By the way, what I want to do is to be able to think in advance which model will fit, like ml_map provided by Scikit-learn. Where can I get this information? I searched a few papers, but couldn't find anything that contained more detailed information other than that SVM was suitable for text classification. How do I study to get prior knowledge like this ml_map?
How do I study to get prior knowledge like this ml_map?
Try to work with different example datasets on different data types by using different algorithms. There are hundreds to be explored. Once you get the good grasp of how they work, it will become more clear. And do not forget to try googling something like advantages of algorithm X, it helps a lot.
And here are my thoughts, I think I used to ask such questions before and I hope it can help if you are struggling: The more you work on different Machine Learning models for a specific problem, you will soon realize that data and feature engineering play the more important parts than the algorithms themselves. The road map provided by scikit-learn gives you a good view of what group of algorithms to use to deal with certain types of data and that is a good start. The boundaries between them, however, are rather subtle. In other words, one problem can be solved by different approaches depending on how you organize and engineer your data.
To sum it up, in order to achieve a good out-of-sample (i.e., good generalization) performance while solving a problem, it is mandatory to look at the training/testing process with different setting combinations and be mindful with your data (for example, answer this question: does it cover most samples in terms of distribution in the wild or just a portion of it?)
I asked myself the question whether most people normally code the machine learning algorithms themselves or whether they are likely to use existing solutions like Weka or R packages.
Of course it depends on the problem - but let's say that I want to use a common solution like a neural network. Is there still a reason to code it myself? To understand the mechanism better and adapt it? Or is the thought of standardized solutions more important?
This is not a good question for Stackoverflow. It's an opinion question, not a programming problem.
Nevertheless, here is my take:
It depends on what you want to do.
If you want to find which algorithm works best for your data problem at hand, try ELKI, Weka, R, Matlab, SciPy, whatever. Try out all the algorithms you can find, and spend even more time on preprocessing your data.
If you know which algorithm you need and need to get it into production, many of these tools will not perform good enough or be easy enough to integrate. Instead, check if you can find low level libraries such as libSVM that provide the functionality you need. If these don't exist, roll your own optimized code.
If you want to do research in this domain, you are best off with extending the existing tools. ELKI and Weka have APIs that you can plug into to provide extensions. R doesn't really have an API (CRAN it's a mess...) but people just dump their code somewhere and (hopefully) add a manual how to use it. Extending these frameworks can save you a lot of effort: you have comparison methods ready to use, and you can re-use a lot of their code. ELKI for example has a lot of index structures to accelerate algorithms. Most of the time, the index acceleration is much harder to write than the actual algorithm. So if you can reuse the existing indexes, this will make your algorithms much faster, too (and you will also benefit from future enhancements to these frameworks).
If you want to learn about existing algorithms you better implement them yourself. You'll be surprised how much more there is to optimizing some algorithms than what is taught in class. E.g. APRIORI. The basic idea is quite simple. But getting all the pruning details right, I say 1 out of 20 students gets these details. If you implement APRIORI, then benchmark it against a known good implementation and try to understand why yours is much slower, then you'll actually discover the subtle details to the algorithms. And don't be surprised to see a factor of 100 performance difference between ELKI, R, Weka etc. - it's can still be the same algorithm, just implemented more or less efficiently when it comes to actual data structures used, memory layout etc.
I want to recommend items that are tagged and are categorized into three price categories (cheap, regular and expensive). I know that with Mahout recommendation could be achieved but here's why I don't know how to use it.
Mahout is based on the other users opinion but all of the new items that I want to recommend are just the new ones that don't have any preferences set yet.
Is Mahout the right tool for this? Is this content-based? (which mahout don't support yet????) or should I use classification?
Thanks!
Since I've never built any recommender system - do not take this answer very seriously (no-one has answered it, so I try)
recommendation system has to be built on some already known (or partially known data). If you have only new (unseen) data there is only possibility to use some clustering algorithm in order to build some clusters.
And if those clusters would be ok, they can be used for training some recommendation system.
Mahout is just a tool which implement various ML methods. You can use other tools like Weka, R, ...
If you have no data at all about a new user, there's really nothing you can do to make recommendations, no matter what you do. There is zero input that would differentiate the person from anyone else.
Good systems should however be able to do something reasonable after the first input is available.
This is not a classifier problem by nature, no. It is also not a clustering tool, other answers notwithstanding.
The price categories are not core to any rec process you would use. You have other data presumably, what is it? That's important.
Finally whether or not to use Mahout depends on taste. You would use it if you want to use Java and Hadoop. And in turn you would only consider Hadoop if you had very large input, and few people have that much data (like >10M data points at least).
(Well, not quite -- my recommender pieces in Mahout pre-date Hadoop and are for on-line, smaller-scale applications. You might indeed be interested in this, if you are working in Java.)
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I am wanting some expert guidance here on what the best approach is for me to solve a problem. I have investigated some machine learning, neural networks, and stuff like that. I've investigated weka, some sort of baesian solution.. R.. several different things. I'm not sure how to really proceed, though. Here's my problem.
I have, or will have, a large collection of events.. eventually around 100,000 or so. Each event consists of several (30-50) independent variables, and 1 dependent variable that I care about. Some independent variables are more important than others in determining the dependent variable's value. And, these events are time relevant. Things that occur today are more important than events that occurred 10 years ago.
I'd like to be able to feed some sort of learning engine an event, and have it predict the dependent variable. Then, knowing the real answer for the dependent variable for this event (and all the events that have come along before), I'd like for that to train subsequent guesses.
Once I have an idea of what programming direction to go, I can do the research and figure out how to turn my idea into code. But my background is in parallel programming and not stuff like this, so I'd love to have some suggestions and guidance on this.
Thanks!
Edit: Here's a bit more detail about the problem that I'm trying to solve: It's a pricing problem. Let's say that I'm wanting to predict prices for a random comic book. Price is the only thing I care about. But there are lots of independent variables one could come up with. Is it a Superman comic, or a Hello Kitty comic. How old is it? What's the condition? etc etc. After training for a while, I want to be able to give it information about a comic book I might be considering, and have it give me a reasonable expected value for the comic book. OK. So comic books might be a bogus example. But you get the general idea. So far, from the answers, I'm doing some research on Support vector machines and Naive Bayes. Thanks for all of your help so far.
Sounds like you're a candidate for Support Vector Machines.
Go get libsvm. Read "A practical guide to SVM classification", which they distribute, and is short.
Basically, you're going to take your events, and format them like:
dv1 1:iv1_1 2:iv1_2 3:iv1_3 4:iv1_4 ...
dv2 1:iv2_1 2:iv2_2 3:iv2_3 4:iv2_4 ...
run it through their svm-scale utility, and then use their grid.py script to search for appropriate kernel parameters. The learning algorithm should be able to figure out differing importance of variables, though you might be able to weight things as well. If you think time will be useful, just add time as another independent variable (feature) for the training algorithm to use.
If libsvm can't quite get the accuracy you'd like, consider stepping up to SVMlight. Only ever so slightly harder to deal with, and a lot more options.
Bishop's Pattern Recognition and Machine Learning is probably the first textbook to look to for details on what libsvm and SVMlight are actually doing with your data.
If you have some classified data - a bunch of sample problems paired with their correct answers -, start by training some simple algorithms like K-Nearest-Neighbor and Perceptron and seeing if anything meaningful comes out of it. Don't bother trying to solve it optimally until you know if you can solve it simply or at all.
If you don't have any classified data, or not very much of it, start researching unsupervised learning algorithms.
It sounds like any kind of classifier should work for this problem: find the best class (your dependent variable) for an instance (your events). A simple starting point might be Naive Bayes classification.
This is definitely a machine learning problem. Weka is an excellent choice if you know Java and want a nice GPL lib where all you have to do is select the classifier and write some glue. R is probably not going to cut it for that many instances (events, as you termed it) because it's pretty slow. Furthermore, in R you still need to find or write machine learning libs, though this should be easy given that it's a statistical language.
If you believe that your features (independent variables) are conditionally independent (meaning, independent given the dependent variable), naive Bayes is the perfect classifier, as it is fast, interpretable, accurate and easy to implement. However, with 100,000 instances and only 30-50 features you can likely implement a fairly complex classification scheme that captures a lot of the dependency structure in your data. Your best bet would probably be a support vector machine (SMO in Weka) or a random forest (Yes, it's a silly name, but it helped random forest catch on.) If you want the advantage of easy interpretability of your classifier even at the expense of some accuracy, maybe a straight up J48 decision tree would work. I'd recommend against neural nets, as they're really slow and don't usually work any better in practice than SVMs and random forest.
The book Programming Collective Intelligence has a worked example with source code of a price predictor for laptops which would probably be a good starting point for you.
SVM's are often the best classifier available. It all depends on your problem and your data. For some problems other machine learning algorithms might be better. I have seen problems that neural networks (specifically recurrent neural networks) were better at solving. There is no right answer to this question since it is highly situationally dependent but I agree with dsimcha and Jay that SVM's are the right place to start.
I believe your problem is a regression problem, not a classification problem. The main difference: In classification we are trying to learn the value of a discrete variable, while in regression we are trying to learn the value of a continuous one. The techniques involved may be similar, but the details are different. Linear Regression is what most people try first. There are lots of other regression techniques, if linear regression doesn't do the trick.
You mentioned that you have 30-50 independent variables, and some are more important that the rest. So, assuming that you have historical data (or what we called a training set), you can use PCA (Principal Componenta Analysis) or other dimensionality reduction methods to reduce the number of independent variables. This step is of course optional. Depending on situations, you may get better results by keeping every variables, but add a weight to each one of them based on relevant they are. Here, PCA can help you to compute how "relevant" the variable is.
You also mentioned that events that are occured more recently should be more important. If that's the case, you can weight the recent event higher and the older event lower. Note that the importance of the event doesn't have to grow linearly accoding to time. It may makes more sense if it grow exponentially, so you can play with the numbers here. Or, if you are not lacking of training data, perhaps you can considered dropping off data that are too old.
Like Yuval F said, this does look more like a regression problem rather than a classification problem. Therefore, you can try SVR (Support Vector Regression), which is regression version of SVM (Support Vector Machine).
some other stuff you can try are:
Play around with how you scale the value range of your independent variables. Say, usually [-1...1] or [0...1]. But you can try other ranges to see if they help. Sometimes they do. Most of the time they don't.
If you suspect that there are "hidden" feature vector with a lower dimension, say N << 30 and it's non-linear in nature, you will need non-linear dimensionality reduction. You can read up on kernel PCA or more recently, manifold sculpting.
What you described is a classic classification problem. And in my opinion, why code fresh algorithms at all when you have a tool like Weka around. If I were you, I would run through a list of supervised learning algorithms (I don't completely understand whey people are suggesting unsupervised learning first when this is so clearly a classification problem) using 10-fold (or k-fold) cross validation, which is the default in Weka if I remember, and see what results you get! I would try:
-Neural Nets
-SVMs
-Decision Trees (this one worked really well for me when I was doing a similar problem)
-Boosting with Decision trees/stumps
-Anything else!
Weka makes things so easy and you really can get some useful information. I just took a machine learning class and I did exactly what you're trying to do with the algorithms above, so I know where you're at. For me the boosting with decision stumps worked amazingly well. (BTW, boosting is actually a meta-algorithm and can be applied to most supervised learning algs to usually enhance their results.)
A nice thing aobut using Decision Trees (if you use the ID3 or similar variety) is that it chooses the attributes to split on in order of how well they differientiate the data - in other words, which attributes determine the classification the quickest basically. So you can check out the tree after running the algorithm and see what attribute of a comic book most strongly determines the price - it should be the root of the tree.
Edit: I think Yuval is right, I wasn't paying attention to the problem of discretizing your price value for the classification. However, I don't know if regression is available in Weka, and you can still pretty easily apply classification techniques to this problem. You need to make classes of price values, as in, a number of ranges of prices for the comics, so that you can have a discrete number (like 1 through 10) that represents the price of the comic. Then you can easily run classification it.