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Let me just start by saying I only took the undergrad AI class at school so I know just enough to be dangerous.
Here's the problem I'm looking to solve...accurate credit scoring is a key part to the success of my business. Currently we rely on a team of actuaries and statistical analysis to suss out patterns in the few dozen variables we track about each individual that indicate that they may be a low or high credit risk. As I understand it this is exactly the type of job that neural nets are great at solving, that is, finding high order relationships across many inputs that a human would likely never spot and then rendering a decision or output that is on average more accurate than what a trained human could do. In short, I want to be able to input your name, address, marital status, what car you drive, where you work, hair color, favorite food, etc in and get a credit score back.
My question is what type or architecture for a neural network would be best for this particular problem. I've done a bit of research and it seems I'm generating questions faster than I'm finding answers at this point. The best I've been able to come up with is some kind of generative deep neural network with multiple hidden layers where each layer is able to abstract one level beyond the previous one. Im assuming it's going to be feed-forward just because it seems to be the default. We have historical data on all previous customers including the information we used to make the initial score as well as data on what type of credit risk they actually turned out to be. This would seem to lend itself to unsupervised learning. Where I'm lost is in number of layers, how the layers are different from each other, size of each layer, connectedness of each of the perceptrons and so on. The more I dig the more I'm getting into research papers that are over my head so I just need some smart person to point me in the right direction
Does anyone have any ideas? Again, I don't need a thorough explanation just a general area I should focus on.
This is supervised learning since you have actual data that can be labelled. It's also feedforward since you're not predicting time series but assigning scores. Further, you should probably just prepare your data (assigning credit scores manually or with some rough heuristic) and start experimenting with some tools before you invest time into implementing state-of-the-art architectures. A multi-layer-perceptron (MLP) with 1 hidden layer is a sufficient starting point for such a problem. From there on, you can train the network to generalize your credit assignment heuristic you began with.
You should know that most "new" architectures you probably read about while researching are dealing with much more difficult problems than credit scoring (speech/image/character recognition/detection). There is a collection of papers on the scenario of credit scoring / risk classification, so I'd recommend reshifting your focus from architectures to actual case studies (see e.g. this paper). Just pick a recent paper with MLPs and apply their parameters. Start simple and improve the system incrementally (as #roganjosh stated).
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I have using Neural Network for a classification problem and I am now at the point to tune all the hyperparameters.
For now, I saw many different hyperparameters that I have to tune :
Learning rate
batch-size
number of iterations (epoch)
For now, my tuning is quite "manual" and I am not sure I am not doing everything in a proper way. Is there a special order to tune the parameters? E.g learning rate first, then batch size, then ... I am not sure that all these parameters are independent. Which ones are clearly independent and which ones are clearly not independent? Should we then tune them together? Is there any paper or article which talks about properly tuning all the parameters in a special order?
There is even more than that! E.g. the number of layers, the number of neurons per layer, which optimizer to chose, etc...
So the real work in training a neural network is actually finding the best-suited parameters.
I would say there is no clear guideline because training a machine learning algorithm, in general, is always task-specific.
You see, there are many hyperparameters to tune, and you won't have time to try out every combination of each. For many hyperparameters, you will build somewhat of intuition on what a good choice would be, but for now, a great starting point is always using what has been proven by others to work. So if you find a paper on the same or similar task you could try to use the same or similar parameters as them too.
Just to share with you some small experiences I've made:
I rarely vary the learning rate. I mostly choose the Adam optimizer and stick with it.
The batch size I try to choose as big as possible without running out of memory
number of iterations you could just set to e.g. 1000. You can always look at the current loss and decide for yourself if you can stop when the net e.g. isn't learning anymore.
Keep in mind these are in no way rules or strict guidelines. Just some ideas until you've got a better intuition yourself. The more papers you've read and more nets you've trained you will understand what to chose when better.
Hope this serves a good starting point at least.
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Lets say I have database with over 1 Million bets (all kinds of sports) made by couple thousands of users, over a period of 2 years (and still growing).
These data are just lying around doing nothing, so I thought if it would be possible to use something like https://www.tensorflow.org/, do a bit of tinkering and it would analyze all the bets in database and learn from it some patterns, whats good and whats not.
The point being is we dont have resources to employ dozens of people for god knows how long to write some complicated software from the ground up. So I was thinking we could use some module from TensorFlow and go from there.
So then I would feed the network with new open bets that are currently in the system (those would be bets that are on matches that are about to be played) and it would pick for me what I should bet on, for example there is a 90% chance this bet will win, because 10 very successful players made this bet, and they have very high success when betting on this particular sport.
We have lots of experienced users, they make lots of money from betting. So the system could be trained on the data we have and then it would know, for example, if user A bets on this league/team, its very likely he will win.
The question is, where do we go from here? Can anybody point us in the right direction? Or is this just too difficult to do, for 2 people in few months? Can we use some pre-programmed solutions, like TensorFlow?
Without having a look of the data, it is impossible to suggest what direction should you take your next steps but anyway your first step should be to explore your data throughly, create model on small subset of data and test your hypothesis.
Overall you can try to:
Use Python or R to Load and Clean Data
Take a random subset of data(some 10,000 rows) and create a simple model using SVM or Random Forest looks like a classification Win/Lose.
Test your results and verify your hypothesis with some data.
Explore about your data to see if you can generate better features
Design a small neural network first and then think about a deep neural network using tensorflow or keras etc.
Have a look at this: https://hackernoon.com/how-to-create-your-own-machine-learning-predictive-system-in-the-nba-using-python-7189d964a371
Yes, this is possible but can be more difficult than it appears.
Consider Microsoft's Cortana which (while only picking if a game will win outright and not ATS) is only approx. 63% accurate; which is quite good but not exactly 90% as you mention in your question (1).
The size of your database should be great for ANN models. It would be a very interesting project for sure!
To your question "where I go from here..." my answer is to simply explore the data in RStudio or using a cloud service such as Microsoft's Azure ML Studio (2) or Amazon's Machine Learning services (3).
Good luck!
Ref. 1: http://www.businessinsider.com/nfl-picks-microsoft-cortana-elo-week-5-2017-10
Ref. 2: https://studio.azureml.net/
Ref. 3: https://aws.amazon.com/amazon-ai/
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I'm looking for some help modelling this machine learning problem.
A hand consists of three rows (containing 3, 5, and 5 cards respectively). Your goal is to build a hand that scores the most points. You receive the cards in intervals called streets, five cards in the first street, and three in the next four streets (you must discard one of the cards in the final four streets). Cards can't be moved once you place them. More details on scoring.
My goal is to build a system that, given a set of streets, plays the hand similar to our best players. It seem pretty clear that I'll need to build a neural network for each street, using features based on the existing hand and the set of cards in the street. I've got plenty of data (streets, placements, and final score), but I'm a little unsure how to model the problem given that the possible outputs are unique on the set of cards (although there are less than 3^5 placements in the first street, and 3^3 after). I've previously only dealt with classification problems with fixed categories.
Does anyone have an example of a similar problem or suggestions how to prepare the training data when you have unique outputs?
A vague question gives a vague answer (which is my excuse for being too lazy to code ;-).
You wrote you have a lot of data, and it seems you want to map the game onto experience gained with supervised learning. But that is not the way game-optimization works. One usually does not perform supervised learning, but rather reinforcement learning. The differences are subtle, but reinforcement learning (with Markov decision processes as its theoretical basis) offers more a local view -- like optimize the decision given a specific state. Supervised learning rather corresponds to optimize several decisions at once.
Another show stopper for the usual supervised learning approach is that even if you have a lot of data, it will almost surely be too little. And it will not offer the "required paths".
The usual approach at least since Thesauro's backgammon player is rather: set up the basic rules of the game, possibly introduce human knowledge as heuristics, and then let the program play against itself as often as possible -- this is how google deep mind set up a master go player, for example. See also this interesting video.
In your case, the task should in principle be not that hard, as there is a comparatively small number of game states and, importantly, any issues involved by psychology like bluffing, consistent playing, and so on are completely absent.
So again: build a bot which can play against itself. One common basis is a function Q(S,a) which assigns to any game state and possible action of the player a value -- this is called Q-learning. And this function is often implemented as a neural network ... although I would think it does not need to be that sophisticated here.
I'll stay that vague for now. But I would be glad to assist you further if necessary.
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I have 2 sequences of numbers and I'd want to continue it using neural algorithms (there is some logic in them, but I don't know what, and there are no external factors affecting the selection). There are some relationship is in each of the two sequences separately, as well as between them.
So, I'm new to machine learning, but I've got such an idea: is there any already written-and-well-working applications (libraries) that implement exact algorithms for me not to learn them all before using. Simply like "most-frequently-used-neural-algorithms-kit".
I'm thinking of analysing some music sheets and two sequences: "notes" and "durations".
OK, according to the comments I think I got what you want.
Generally, no, you don't need to rewrite the standard algorithm of ANN. But be aware that ANN is not an algorithm, but a cluster of algorithms (including BackPropagation-ANN, Hopfield-ANN, Boltzmann Machine etc). Among them I recommend BP-ANN which is simple and suitable for your project. You might want to input a sequences of the known notes and duration, and then expect an output of the next note and duration.
To use BP-ANN, you don't need to rewrite them. Due to its a widely-used algorithm, there are many toolkits and open source implementations of it:
Google "back propagation neural network implementation", you will find it easily. There are also a few opensource projects on Github(in both C language and Matlab): https://github.com/search?q=back+propagation&type=Everything&repo=&langOverride=&start_value=1
For further reading if you also want to deeply understand the details of its implementation, read this: http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1279&context=ecetr&sei-redir=1
If you're interested in neural networks there are plenty of libraries available.
ANNIE is one such open source example, the MATLAB Neural Network toolbox is a
commercial example. These are libraries which you tell the architecture of the
neural network, you can train, test, verify, etc. The important part in all
these machine learning methods is how you represent your data, and those were
the comments you were getting (for example Predictor's). Sometimes you get
excellent results with one representation and very bad results with others.
There are also libraries to train SVMs (a specialized algorithm to train neural
networks) with quadratic regularization, LIBSVM is one great example.
There is also plenty of work on predicting time series with neural networks (if
that is what you want to do with music, I am not sure what exactly you want).
If the input is a series of (note, duration) pairs, then I suspect you'd get much farther by summarizing the historical note-to-note transitions or by something similar in an effort to capture the syntax of the music (Markov analysis, etc.), than you would by stuffing this into a neural network. It may help, too, to try representing the series as note differentials, measuring how many notes up or down the scale the new note is, rather than the actual value of the note itself.
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I am interested in doing some Collective Intelligence programming, but wonder how it can work?
It is said to be able to give accurate predictions: the O'Reilly Programming Collective Intelligence book, for example, says a collection of traders' action actually can predict future prices (such as corn) better than an expert can.
Now we also know in statistics class that, if it is a room of 40 students taking exam, there will be 3 to 5 students who will get an "A" grade. There might be 8 that get "B", and 17 that got "C", and so on. That is, basically, a bell curve.
So from these two standpoints, how can a collection of "B" and "C" answers give a better prediction than the answer that got an "A"?
Note that the corn price, for example, is the accurate price factoring in weather, demand of food companies using corn, etc, rather than "self fulfilling prophecy" (more people buy the corn futures and price goes up and more people buy the futures again). It is actually predicting the supply and demand accurately to give out an accurate price in the future.
How is it possible?
Update: can we say Collective Intelligence won't work in stock market euphoria and panic?
The Wisdom of Crowds wiki page offers a good explanation.
In short, you don't always get good answers. There needs to be a few conditions for it to occur.
Well, you might want to think of the following "model" for a guess:
guess = right answer + error
If we ask a lot of people a question, we'll get lots of different guesses. But if, for some reason, the distribution of errors is symmetric around zero (actually it just has to have zero mean) then the average of the guesses will be a pretty good predictor of the right answer.
Note that the guesses don't necessarily have to be good -- i.e., the errors could indeed be large (grade B or C, rather than A) as long as there are grade B and C answers distributed on both sides of the right answer.
Of course, there are cases where this is a terrible model for our guesses, so collective intelligence won't always work...
Crowd Wisdom techniques, like prediction markets, work well in some situations, and poorly in others, just as other approaches (experts, for instance) have their strengths and weaknesses. The optimal arenas therefore, are ones where no other approaches do very well, and prediction markets can do well. Some examples include predicting public elections, estimating project completion dates, and predicting the prevalence of epidemics. These are areas where information is spread around sparsely, and experts haven't found effective models that reliably predict.
The general idea is that market participants make up for one another's weaknesses. The expectation isn't that the markets will always predict every outcome correctly, but that, due to people noticing other people's mistakes, they won't miss crucial information as often, and that over the long haul, they'll do better. In cases where the exerts actually know the answer, they'll be able to influence the outcome. Different experts can weigh in on different questions, so each has more influence where they have the most knowledge. And as markets continue over time, each participant gets feedback from their gains and losses that makes them better informed about which kinds of questions they actually understand and which ones they should stay away from.
In a classroom, people are often graded on a curve, so the distribution of grades doesn't tell you much about how good the answers were. Prediction markets calibrate all the answers against actual outcomes. This public record of successes and failures does a lot to reinforce the mechanism, and is missing in most other approaches to forecasting.
Collective intelligence is really good at coming up to to problems that have complex behavior behind them, because they are able to take multiple sources of opinions/attributes to determine the end result. With a setup like this, training helps to optimize the end result of the processes.
The fault is in your analogy, both opinions are not equal. Traders predict direct profit for their transaction (the little part of the market they have overview over), while the expert tries to predict the overall field.
IOW the overall traders position is pieced together like a jigsaw puzzle based on a large amount of small opinions for their respective piece of the pie (where they are assumed to be experts).
A single mind can't process that kind of detail, which is why the overall position MIGHT overshadow the real expert. Note that this is particularly phenomon is usually restricted to a fairly static market, not in periods of turmoil. Expert usually do better then, since they are often better trained and motivated to avoid going with general sentiment. (which is often comparable to that of a lemming in times of turmoil)
The problem with the class analogy is that the grading system doesn't assume that the students are masters in their (difficult to predict) terrain, so it is not comparable.
P.s. note that the base axiom depends on all players being experts in a small piece of the field. One can debate if this requirement actually transports well to a web 2 environment.