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The State of Data Science and Machine Learning

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Re: The State of Data Science and Machine Learning

#31

Post by followmeon »

kevinnk wrote: post #24
followmeon wrote: post #22

Earlier quoted context omitted.

Dirty data is not as much as a problem for me than human-biased data. Dirty data engineering, like modeling, will soon be largely automated. Let's say you are predicting store sales. You create a feature that holds the store sales of one year back. The feature works really well and you are happy with your evaluation. But you captured bias: The previous model the store used was "predict today's sales by looking at las…

If I understand you correctly, the way you'd address this is by using counterfactuals. See this course[1] for an overview and this paper[2] which talks about the bias problem in the context of movie recommendations. [1] http://www.cs.cornell.edu/courses/cs7792/2016fa/ [2] http://www.cs.cornell.edu/people/tj/publications/schnabel_et...

Yes, counterfactual inference is relevant to this. But it is not so much about answering "what would have happened if?", but more about control theory and feedback loops: Your model never being a static function, but a node inside a giant recursive net composed of other models and humans.

Another example (this time on the output-end): You build a model to route emails to sets of experts inside an organization. Your proxy loss is multi-class logistic loss on topic classes. You are interested in improving response times (which you can more or less measure in aggregate) and quality of response (which is harder to measure, if at all).

You build a first iteration of the model and response times improve. Then you create new features and modeling techniques and you improve logistic loss, but when you deploy this model, response times go way down. What happened? Maybe the experts fitted/adapted to the model output: They learned how to quickly answer a specific type of email because it keeps getting routed to them. The new model does better matching topics to emails, resulting in those emails now being send to another expert. While this expert in the long-term may become better at answering emails closer to his/her topic expertise, in a faster and more informative manner, in the short-term he/she will be slower and of lower quality, as they need to adapt to the new types of emails they are getting, and lack the priors for dealing with ambiguous emails.

Both on the input and the output of models there are all sorts of these nasty human-feedback loops that are very hard to even identify and harder to solve.

Leon Bottou gave a talk about these challenges in the context of ML at Facebook: http://leon.bottou.org/slides/2challenges/2challenges.pdf (he mentions the decisions of two separate ML teams adversely influencing their individual experiments). This paper (https://research.google.com/pubs/pub43146.html) talks about "hidden feedback loops" and "entanglement".

Re: The State of Data Science and Machine Learning

#32

Post by followmeon »

apohn wrote: post #25
followmeon wrote: post #22

Earlier quoted context omitted.

Dirty data is not as much as a problem for me than human-biased data. Dirty data engineering, like modeling, will soon be largely automated. Let's say you are predicting store sales. You create a feature that holds the store sales of one year back. The feature works really well and you are happy with your evaluation. But you captured bias: The previous model the store used was "predict today's sales by looking at las…

>Dirty data engineering, like modeling, will soon be largely automated. I don't agree. For every modern tech company that collects data that lends itself to automated data cleaning, there's a 40+ year old company that defined what data to be collected in 1990, designed an "automated system" in 1995 and has been shoehorning improvements on that system since then. At my last job I was given access to a database with 15…

It seems to me you were given three jobs: database admin, data engineer, and data scientist.

When I am talking about automated data cleaning, I am talking more about preprocessing text, dealing with missing variables, discarding duplicates, noisy/uninformative variable and outlier removal, spelling correction, feature interactions and transformations. All of these can be (and are being) largely automated. [1] [2]

A data lake with 150+ undocumented tables is garbage in-garbage out, both for machines and humans. I'd almost label that as the barrier: "Data not available", not: "Dirty data". While a reality for some companies, such a company really needs a DB admin or data engineer, not try to shoehorn an (expensive) data scientist in these roles.

[1] https://people.csail.mit.edu/kalyan/dsm/

[2] https://www.ijcai.org/proceedings/2017/352

Re: The State of Data Science and Machine Learning

#33

Post by jerednel »

godelski wrote: post #27

Anyone else find it weird that when you click "other" for gender that the data looks more like garbage? I was trying to actually compare male and female salaries out of interest but have a hard time believing so many people earn Has anyone sorted through this data and tried to account for these factors? I'd be interested at the uncertainty and how the information was gathered.

I pulled a few gender stats here. http://bit.ly/2zjrSJD Accounting for country, education, and industry you really reduce the population you're sampling from but those deviations are huge. You need to account for industry especially.

Re: The State of Data Science and Machine Learning

#34

Post by eanzenberg »

kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

That's precisely the problem of Kaggle. The data is mostly cleaned for you. This is most of the job of a DS in industry. Cleaning your data improves performance way more than working hard on optimizing your ML algo.

Re: The State of Data Science and Machine Learning

#35

Post by IanCal »

radarsat1 wrote: post #28
kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

Of course everyone agrees that "cleaning data" is difficult and boring, and it's always mentioned, but what I don't really understand is what kind of tools people expect for this beyond what are already available. E.g. pandas is pretty good at merging tables, re-ordering, finding doubles, filling or dropping unknowns etc. There are also tools for visualizing large amounts of data, look for outliers, etc. Beyond the b…

I'm starting to build up various utilities to help with this kind of thing, but I fully agree. The decisions require understanding the business requirements (do I use source X or Y for field 1, what errors are OK, what types of error are worst, etc), but the process of finding some of these could be better.

One simple one is missing data. Missing data is rarely a null, I've seen (on one field, in one dataset):

    N/A
    NA
    " "
    Blank # literally the string "Blank"
    NULL # Again, the string
    No data
    No! Data
    No data was entered for this field
    No data is known
    The data is not known
    There is no data
And many, many more. None can be clearly identified automatically, but some processes like:

Pull out the most common items, manually mark some as "equivalent to blank" and remove.

Identify common substrings with known text (N/A, NULL, etc) and bring up those examples.

Are useful, I'd like to extend with more clustering and analysis to bring out other common general issues but rare specific issues. Lots of similar things with encodings, etc. too.

Other things that might be good are clearer ways I could supply general conditions I expect to hold true, then bring the most egregious ones to my attention so I can either clear out / deal with them in some way. A good way of recording issues that have already been analysed and found to be OK would be great too.

Re: The State of Data Science and Machine Learning

#36

Post by ScottBurson »

kmax12 wrote: post #29
radarsat1 wrote: post #28

Earlier quoted context omitted.

Of course everyone agrees that "cleaning data" is difficult and boring, and it's always mentioned, but what I don't really understand is what kind of tools people expect for this beyond what are already available. E.g. pandas is pretty good at merging tables, re-ordering, finding doubles, filling or dropping unknowns etc. There are also tools for visualizing large amounts of data, look for outliers, etc. Beyond the b…

I am the lead contributor of a python library called Featuretools[0]. It is intended to perform automated feature engineering on time-varying and multi-table datasets. We see it as bridging a gap between pandas and libraries for machine learning like scikit-learn. It doesn't handle data cleaning necessarily, but it does help get raw datasets ready for machine learning algorithms. We have actually used it to compete o…

Wow, this looks very cool!

Re: The State of Data Science and Machine Learning

#37

Post by itronitron »

apohn wrote: post #23
zebrafish wrote: post #17

Earlier quoted context omitted.

I agree with your assessment that a lot of times the business problems have been put into a form that lends itself to exploitation by machine learning. Sometimes a company has a lot of data that's actually useless. Most of the time, I've found that business people do not understand the value of data. Often I hear, "we have this data set, let's unleash the data scientist on this to tell us something." or "we have this…

>I spend a lot of my time explaining that there must first be a business objective, a key question, or hypothesis that can then be understood through data. I cannot take a haystack and find the needle that is interesting to you. And if I do find that needle, many times there are no resulting changes made to our strategy. IMO a number of data science positions should be considered partly research positions. You are hi…

The organizations I have seen that do best at this have teams of data scientists collaborating with devs/engineers and business analysts... there need to be a lot of different research activities going on most of which are working off the same data/compute infrastructure but with some people dissatisfied and pushing the edge of course. Also regarding hiring pipelines I would discourage hiring based on technology keywords as anyone that is a good fit should be intelligent and curious enough to pick up their new employer's tech stack relatively quickly.

Re: The State of Data Science and Machine Learning

#38

Post by glial »

With the rise of Tensorflow and sklearn, the strong Python showing makes sense.

However, I wish Python had a solid IDE for interactive work like RStudio. Jupyter notebooks are fine but being able to easily inspect variables is super convenient.

Spyder doesn't cut it. Y-hat's Rodeo was still a bit buggy last time I tried it. Any other suggestions?

Re: The State of Data Science and Machine Learning

#39

Post by godelski »

jerednel wrote: post #33
godelski wrote: post #27

Anyone else find it weird that when you click "other" for gender that the data looks more like garbage? I was trying to actually compare male and female salaries out of interest but have a hard time believing so many people earn Has anyone sorted through this data and tried to account for these factors? I'd be interested at the uncertainty and how the information was gathered.

I pulled a few gender stats here. http://bit.ly/2zjrSJD Accounting for country, education, and industry you really reduce the population you're sampling from but those deviations are huge. You need to account for industry especially.

Well this really doesn't discuss the error associated with the data. Which is what I was trying to get at. There seems to be a lot associated with it, which makes accurate predictions difficult to make.

Re: The State of Data Science and Machine Learning

#40

Post by philvb »

radarsat1 wrote: post #28
kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

Of course everyone agrees that "cleaning data" is difficult and boring, and it's always mentioned, but what I don't really understand is what kind of tools people expect for this beyond what are already available. E.g. pandas is pretty good at merging tables, re-ordering, finding doubles, filling or dropping unknowns etc. There are also tools for visualizing large amounts of data, look for outliers, etc. Beyond the b…

Yes, completely agree that each dataset requires decisions to be made that can't be automated, but there are huge opportunities for tools to assist users in understanding what cleaning decisions they might want to make and how those decisions affect the data. Most data cleaning tools do a very poor job of helping the user visualize and understand the impact cleaning has on data - they're usually very low level (such as pandas).

As an example of a tool: Trifacta (disclaimer I work here) https://www.trifacta.com/products/wrangler/. We're trying to improve data cleaning with features such as suggesting transforms the user might want, integrating data profiling through all stages to discover and understand, and transform previews so the user can understand the impact.

I think there's a huge opportunity for better tools in the problem space.

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