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I trained data from 500 devices to predict their performance. Then I applied my trained model to a test data set for another 500 devices and show pretty good prediction results. Now my executives want me to prove this model will work well on one million devices not only on 500. Obviously we don't have data for one million devices. And if the model is not reliable, they want me to discover the required amount of train data in order to make a reliable prediction on one million devices. How should I deal with these executives who don't have a background in statistical analysis and modeling? Any suggestions? Thanks
I have suggested to #cep to write up his comment as an answer - including providing the variance and bias calculations. In any case it could be added
"Do not be quick to assume Execs are essentially incapable in terms of
technical or mathematical concepts"
While there may be Dilbert managers out there .. somewhere I have seen few of them myself. More often managers get to their positions through hard work. They are likely to be rusty - but the abilities are likely still there.
In this case whether or not they have a "background in statistical analysis and modeling" they are applying common sense.
The first thing you might do is to provide the proper context and terminology. #cel has mentioned some of it: providing concrete values for :
assumptions
what assumptions are you making about the data.
What basis is there to consider extrapolation of the limited data
why should said extrapoated results be trusted to apply to the 99.5% of untested data
data distribution
basic descriptive statistics
your take on the apriori distribution of the data. Justify why you chose it
modeling
which models/approaches were considered and why
which model you actually chose and why
how did you arrive at the hyperparameters
how you trained the model
results
statistical measures of fit and error rate
This is my problem description:
"According to the Survey on Household Income and Wealth, we need to find out the top 10% households with the most income and expenditures. However, we know that these collected data is not reliable due to many misstatements. Despite these misstatements, we have some features in the dataset which are certainly reliable. But these certain features are just a little part of information for each household wealth."
Unreliable data means that households tell lies to government. These households misstate their income and wealth in order to unfairly get more governmental services. Therefore, these fraudulent statements in original data will lead to incorrect results and patterns.
Now, I have below questions:
How should we deal with unreliable data in data science?
Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
-How can we evaluate our errors in this study? Since we have unlabeled dataset, should I look for labeling techniques? Or, should I use unsupervised methods? Or, should I work with semi-supervised learning methods?
Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
Please introduce me any ideas or references which can help me in this issue.
Thanks in advance.
Q: How should we deal with unreliable data in data science
A: Use feature engineering to fix unreliable data (make some transformations on unreliable data to make it reliable) or drop them out completely - bad features could significantly decrease the quality of the model
Q: Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
A: ML algorithms are not magic sticks, they can't figure out anything unless you tell them what you are looking for. Can you describe what means 'unreliable'? If yes, you can, as I mentioned, use feature engineering or write a code which will fix the data. Otherwise no ML algorithm will be able to help you, without the description of what exactly you want to achieve
Q: Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
A: I don't think so just because the question itself is too open-ended. What means 'the quality of the data'?
Generally, here are couple of things for you to consider:
1) Spend some time on googling feature engineering guides. They cover how to prepare your data for you ML algorithms, refine it, fix it. Good data with good features dramatically increase the results.
2) You don't need to use all of features from original data. Some of features of original dataset are meaningless and you don't need to use them. Try to run gradient boosting machine or random forest classifier from scikit-learn on your dataset to perform classification (or regression, if you do regression). These algorithms also evaluate importance of each feature of original dataset. Part of your features will have extremely low importance for classification, so you may wish to drop them out completely or try to combine unimportant features together somehow to produce something more important.
People often throw around the terms IR, ML, and data mining, but I have noticed a lot of overlap between them.
From people with experience in these fields, what exactly draws the line between these?
This is just the view of one person (formally trained in ML); others might see things quite differently.
Machine Learning is probably the most homogeneous of these three terms, and the most consistently applied--it's limited to the pattern-extraction (or pattern-matching) algorithms themselves.
Of the terms you mentioned, "Machine Learning" is the one most used by Academic Departments to describe their Curricula, their academic departments, and their research programs, as well as the term most used in academic journals and conferences proceedings. ML is clearly the least context-dependent of the terms you mentioned.
Information Retrieval and Data Mining are much closer to describing complete commercial processes--i.e., from user query to retrieval/delivery of relevant results. ML algorithms might be somewhere in that process flow, and in the more sophisticated applications, often are, but that's not a formal requirement. In addition, the term Data Mining seems usually to refer to application of some process flow on big data (i.e, > 2BG) and therefore usually includes a distributed processing (map-reduce) component near the front of that workflow.
So Information Retrieval (IR) and Data Mining (DM) are related to Machine Learning (ML) in an Infrastructure-Algorithm kind of way. In other words, Machine Learning is one source of tools used to solve problems in Information Retrieval. But it's only one source of tools. But IR doesn't depend on ML--for instance, a particular IR project might be storage and rapid retrieval of the fully-indexed data responsive to a user's search query IR, the crux of which is optimizing performance of the data flow, i.e., the round-trip from query to delivering the search results to the user. Prediction or pattern matching might not be useful here. Likewise, a DM project might use an ML algorithm for the predictive engine, yet a DM project is more likely to also be concerned with the entire processing flow--for instance, parallel computation techniques for efficient input of an enormous data volume (TB perhaps) which delivers a proto-result to a processing engine for computation of descriptive statistics (mean, standard deviation, distribution, etc. on the variables (columns).
Lastly consider the Netflix Prize. This competition was directed solely to Machine Learning--the focus was on the prediction algorithm, as evidenced by the fact that there was a single success criterion: accuracy of the predictions returned by the algorithm. Imagine if the 'Netflix Prize' were rebranded as a Data Mining competition. The success criteria would almost certainly be expanded to more accurately access the algorithm's performance in the actual commercial setting--so for instance overall execution speed (how quickly are the recommendations delivered to the user) would probably be considered along with accuracy.
The terms "Information Retrieval" and "Data Mining" are now in mainstream use, though for a while I only saw these terms in my job description or in vendor literature (usually next to the word "solution.") At my employer, we recently hired a "Data Mining" analyst. I don't know what he does exactly, but he wears a tie to work every day.
I'd try to draw the line as follows:
Information retrieval is about finding something that already is part of your data, as fast as possible.
Machine learning are techniques to generalize existing knowledge to new data, as accurate as possible.
Data mining is primarly about discovering something hidden in your data, that you did not know before, as "new" as possible.
They intersect and often use techniques of one another. DM and IR both use index structures to accelerate processes. DM uses a lot of ML techniques, for example a pattern in the data set that is useful for generalization might be a new knowledge.
They are often hard to separate. Do yourself a favor and don't just go for the buzzwords. In my opinion the best way of distinguishing them is by their intention, as given above: find data, generalize to new data, find new properties of existing data.
You can also add pattern recognition and (computational?) statistics as another couple of areas that overlap with the three you mentioned.
I'd say there is no well-defined line between them. What separates them is their history and their emphases. Statistics emphasizes mathematical rigor, data mining emphasizes scaling to large datasets, ML is somewhere in between.
Data mining is about discovering hidden patterns or unknown knowledge, which can be used
for decision making by people.
Machine learning is about learning a model to classify new objects.
Given a set of data very similar to the Motley Fool CAPS system, where individual users enter BUY and SELL recommendations on various equities. What I would like to do is show each recommendation and I guess some how rate (1-5) as to whether it was good predictor<5> (ie. correlation coefficient = 1) of the future stock price (or eps or whatever) or a horrible predictor (ie. correlation coefficient = -1) or somewhere in between.
Each recommendation is tagged to a particular user, so that can be tracked over time. I can also track market direction (bullish / bearish) based off of something like sp500 price. The components I think that would make sense in the model would be:
user
direction (long/short)
market direction
sector of stock
The thought is that some users are better in bull markets than bear (and vice versa), and some are better at shorts than longs- and then a combination the above. I can automatically tag the market direction and sector (based off the market at the time and the equity being recommended).
The thought is that I could present a series of screens and allow me to rank each individual recommendation by displaying available data absolute, market and sector out performance for a specific time period out. I would follow a detailed list for ranking the stocks so that the ranking is as objective as possible. My assumption is that a single user is right no more than 57% of the time - but who knows.
I could load the system and say "Lets rank the recommendation as a predictor of stock value 90 days forward"; and that would represent a very explicit set of rankings.
NOW here is the crux - I want to create some sort of machine learning algorithm that can identify patterns over a series of time so that as recommendations stream into the application we maintain a ranking of that stock (ie. similar to correlation coefficient) as to the likelihood of that recommendation (in addition to the past series of recommendations ) will affect the price.
Now here is the super crux. I have never taken an AI class / read an AI book / never mind specific to machine learning. So I cam looking for guidance - sample or description of a similar system I could adapt. Place to look for info or any general help. Or even push me in the right direction to get started...
My hope is to implement this with F# and be able to impress my friends with a new skill set in F# with an implementation of machine learning and potentially something (application / source) I can include in a tech portfolio or blog space;
Thank you for any advice in advance.
I have an MBA, and teach data mining at a top grad school.
The term project this year was to predict stock price movements automatically from news reports. One team had 70% accuracy, on a reasonably small sample, which ain't bad.
Regarding your question, a lot of companies have made a lot of money on pair trading (find a pair of assets that normally correlate, and buy/sell pair when they diverge). See the writings of Ed Thorpe, of Beat the Dealer. He's accessible and kinda funny, if not curmudgeonly. He ran a good hedge fund for a long time.
There is probably some room in using data mining to predict companies that will default (be unable to make debt payments) and shorting†them, and use the proceeds to buy shares in companies less likely to default. Look into survival analysis. Search Google Scholar for "predict distress" etc in finance journals.
Also, predicting companies that will lose value after an IPO (and shorting them. edit: Facebook!). There are known biases, in academic literature, that can be exploited.
Also, look into capital structure arbitrage. This is when the value of the stocks in a company suggest one valuation, but the value of the bonds or options suggest another value. Buy the cheap asset, short the expensive one.
Techniques include survival analysis, sequence analysis (Hidden Markov Models, Conditional Random Fields, Sequential Association Rules), and classification/regression.
And for the love of God, please read Fooled By Randomness by Taleb.
†shorting a stock usually involves calling your broker (that you have a good relationship with) and borrowing some shares of a company. Then you sell them to some poor bastard. Wait a while, hopefully the price has gone down, you buy some more of the shares and give them back to your broker.
My Advice to You:
There are several Machine Learning/Artificial Intelligence (ML/AI) branches out there:
http://www-formal.stanford.edu/jmc/whatisai/node2.html
I have only tried genetic programming, but in the "learning from experience" branch you will find neural nets. GP/GA and neural nets seem to be the most commonly explored methodologies for the purpose of stock market predictions, but if you do some data mining on Predict Wall Street, you might be able to utilize a Naive Bayes classifier to do what you're interested in doing.
Spend some time learning about the various ML/AI techniques, get a small data set and try to implement some of those algorithms. Each one will have its strengths and weaknesses, so I would recommend that you try to combine them using Naive Bays classifier (or something similar).
My Experience:
I'm working on the problem for my Masters Thesis so I'll pitch my results using Genetic Programming: www.twitter.com/darwins_finches
I started live trading with real money in 09/09/09.. yes, it was a magical day! I post the GP's predictions before the market opens (i.e. the timestamps on twitter) and I also place the orders before the market opens. The profit for this period has been around 25%, we've consistently beat the Buy & Hold strategy and we're also outperforming the S&P 500 with stocks that are under-performing it.
Some Resources:
Here are some resources that you might want to look into:
Max Dama's blog: http://www.maxdama.com/search/label/Artificial%20Intelligence
My blog: http://mlai-lirik.blogspot.com/
AI Stock Market Forum: http://www.ai-stockmarketforum.com/
Weka is a data mining tool with a collection of ML/AI algorithms: http://www.cs.waikato.ac.nz/ml/weka/
The Chatter:
The general consensus amongst "financial people" is that Artificial Intelligence is a voodoo science, you can't make a computer predict stock prices and you're sure to loose your money if you try doing it. None-the-less, the same people will tell you that just about the only way to make money on the stock market is to build and improve on your own trading strategy and follow it closely.
The idea of AI algorithms is not to build Chip and let him trade for you, but to automate the process of creating strategies.
Fun Facts:
RE: monkeys can pick better than most experts
Apparently rats are pretty good too!
I understand monkeys can pick better than most experts, so why not an AI? Just make it random and call it an "advanced simian Mersenne twister AI" or something.
Much more money is made by the sellers of "money-making" systems then by the users of those systems.
Instead of trying to predict the performance of companies over which you have no control, form a company yourself and fill some need by offering a product or service (yes, your product might be a stock-predicting program, but something a little less theoretical is probably a better idea). Work hard, and your company's own value will rise much quicker than any gambling you'd do on stocks. You'll also have plenty of opportunities to apply programming skills to the myriad of internal requirements your own company will have.
If you want to go down this long, dark, lonesome road of trying to pick stocks you may want to look into data mining techniques using advanced data mining software such as SPSS or SAS or one of the dozen others.
You'll probably want to use a combination or technical indicators and fundamental data. The data will more than likely be highly correlated so a feature reduction technique such as PCA will be needed to reduce the number of features.
Also keep in mind your data will constantly have to be updated, trimmed, shuffled around because market conditions will constantly be changing.
I've done research with this for a grad level class and basically I was somewhat successful at picking whether a stock would go up or down the next day but the number of stocks in my data set was fairly small (200) and it was over a very short time frame with consistent market conditions.
What I'm trying to say is what you want to code has been done in very advanced ways in software that already exists. You should be able to input your data into one of these programs and using either regression, or decision trees or clustering be able to do what you want to do.
I have been thinking of this for a few months.
I am thinking about Random Matrix Theory/Wigner's distribution.
I am also thinking of Kohonen self-learning maps.
These comments on speculation and past performance apply to you as well.
I recently completed my masters thesis on deep learning and stock price forecasting. Basically, the current approach seems to be LSTM and other deep learning models. There are also 10-12 technical indicators (TIs) based on moving average that have been shown to be highly predictive for stock prices, especially indexes such as SP500, NASDAQ, DJI, etc. In fact, there are libraries such as pandas_ta for computing various TIs.
I represent a group of academics that are trying to predict stocks in a general form that can also be applied to anything, even the rating of content.
Our algorithm, which we describe as truth seeking, works as follows.
Basically each participant has their own credence rating. This means that the higher your credence or credibility, then the more their vote counts. Credence is worked out by how close to the weighted credence each vote is. It's like you get a better credence value the closer you get to the average vote that has already been adjusted for credence.
For example, let's say that everyone is predicting that a stock's value will be at value X in 30 day's time (a future's option). People who predict on the average get a better credence. The key here is that the individual doesn't know what the average is, only the system. The system is tweaked further by weighting the guesses so that the target spot that generates the best credence is those votes that are already endowed with more credence. So the smartest people (historically accurate) project the sweet spot that will be used for further defining who gets more credence.
The system can be improved too to adjust over time. For example, when you find out the actual value, those people who guessed it can be rewarded with a higher credence. In cases where you can't know the future outcome, you can still account if the average weighted credence changes in the future. People can be rewarded even more if they spotted the trend early. The point is we don't need to even know the outcome in the future, just the fact that the weighted rating changed in the future is enough to reward people who betted early on the sweet spot.
Such a system can be used to rate anything from stock prices, currency exchange rates or even content itself.
One such implementation asks people to vote with two parameters. One is their actual vote and the other is an assurity percentage, which basically means how much a particular participant is assured or confident of their vote. In this way, a person with a high credence does not need to risk downgrading their credence when they are not sure of their bet, but at the same time, the bet can be incorporated, it just won't sway the sweet spot as much if a low assurity is used. In the same vein, if the guess is directly on the sweet spot, with a low assurity, they won't gain the benefits as they would have if they had used a high assurity.
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UPDATED: I'm asking this from a development perspective, however to illustrate, a canoical non-development example that comes to mind is that if it costs, say, $10,000
to keep a uptime rate of 99%, then it theoretically can cost $100,000 to keep a rate
of 99.9%, and possibly $1,000,000 to keep a rate of 99.99%.
Somewhat like calculus in approaching 0, as we closely approach 100%,
the cost can increase exponentially. Therefore, as a developer or PM, where do you decide
that the deliverable is "good enough" given the time and monetary constraints, e.g.: are you getting a good ROI at 99%, 99.9%,
99.99%?
I'm using a non-development example because I'm not sure of a solid metric for development. Maybe in the above example "uptime" could be replaced with "function point to defect ratio", or some such reasonable measure rate of bugs vs. the complexity of code. I would also welcome input regarding all stages of a software development lifecycle.
Keep the classic Project Triangle constraints in mind (quality vs. speed vs. cost). And let's assume that the customer wants the best quality you can deliver given the original budget.
There's no way to answer this without knowing what happens when your application goes down.
If someone dies when your application goes down, uptime is worth spending millions or even billions of dollars on (aerospace, medical devices).
If someone may be injured if your software goes down, uptime is worth hundreds of thousands or millions of dollars (industrial control systems, auto safety devices)
If someone looses millions of dollars if your software goes down, uptime is worth spending millions on (financial services, large e-commerce apps).
If someone looses thousands of dollars if your software goes down, uptime is worth spending thousands on (retail, small e-commerce apps).
If someone will swear at the computer and looses productivity while it reboots when your software goes down, then uptime is worth spending thousands on (most internal software).
etc.
Basically take (cost of going down) x (number of times the software will go down) and you know how much to spend on uptime.
The Quality vs Good Enough discussion I've seen has a practical ROI at 95% defect fixes. Obviously show stoppers / critical defects are fixed (and always there are the exceptions like air-plane autopilots etc, that need to not have so many defects).
I can't seem to find the reference to the 95% defect fixes, it is either in Rapid Development or in Applied Software Measurement by Caper Jones.
Here is a link to a useful strategy for attacking code quality:
http://www.gamedev.net/reference/articles/article1050.asp
The client, of course, would likely balk at that number and might say no more than 1 hour of downtime per year is acceptable. That's 12 times more stable. Do you tell the customer, sorry, we can't do that for $100,000, or do you make your best attempt, hoping your analysis was conservative?
Flat out tell the customer what they want isn't reasonable. In order to gain that kind of uptime, a massive amount of money would be needed, and realistically, the chances of reaching that percentage of uptime constantly just isn't possible.
I personally would go back to the customer and tell them that you'll provide them with the best setup with 100k and set up an outage report guideline. Something like, for every outage you have, we will complete an investigation as to why this outage happened, and how what we will do to make the chances of it happening again almost non existent.
I think offering SLAs is just a mistake.
I think the answer to this question depends entirely on the individual application.
Software that has an impact on human safety has much different requirements than, say, an RSS feed reader.
The project triangle is a gross simplification. In lots of cases you can actually save time by improving quality. For example by reducing repairs and avoiding costs in maintenance. This is not only true in software development.Toyota lean production proved that this works in manufacturing too.
The whole process of software development is far too complex to make generalizations on cost vs quality. Quality is a fuzzy concept that consists of multiple factors. Is testable code of higher quality than performant code? Is maintainable code of higher quality than testable code? Do you need testable code for an RSS reader or performant code? And for a fly-by-wire F16?
It's more productive to make informed desisions on a case-by-case basis. And don't be afraid to over-invest in quality. It's usually much cheaper and safer than under-investing.
To answer in an equally simplistic way..
..When you stop hearing from the customers (and not because they stopped using your product).. except for enhancement requests and bouquets :)
And its not a triangle, it has 4 corners - Cost Time Quality and Scope.
To expand on what "17 of 26" said, the answer depends on value to the customer. In the case of critical software, like aircrafct controller applications, the value to the customer of a high quality rating by whatever measure they use is quite high. To the user of an RSS feed reader, the value of high quality is considerably lower.
It's all about the customer (notice I didn't say user - sometimes they're the same, and sometimes they're not).
Chasing the word "Quality" is like chasing the horizon. I have never seen anything (in the IT world or outside) that is 100% quality. There's always room for improvement.
Secondly, "quality" is an overly broad term. It means something different to everyone and subjective in it's degree of implementation.
That being said, every effort boils down to what "engineering" means--making the right choices to balance cost, time and key characteristics (ie. speed, size, shape, weight, etc.) These are constraints.