141–150 of 200 posts
- We use a complex multivariate model to predict customer conversion and prioritize lead response - We use text analysis to improve content for effectiveness and conversion Among other things
Can you please explain complex multivariate model in detail? I am curious to learn about it
Wrote a system for automatically grading kids' essays (think the lame "summarize this passage"-type passages on standardized tests). In that case it was actually a platform for machine learning - ie, plumb together feature modules into modeling modules and compare output model results.
I work in manufacturing. We have an acoustic microscope that scans parts with the goal of identifying internal defects (typically particulate trapped in epoxy bonds). It's pretty hard to define what size/shape/position/number of particles is worthy of failing the device. Our final product test can tell us what product is "good" and "bad" based on electrical measurements, but that test can't be applied at the stage of…
Ok there are some warning signs here. First, bagged decision trees are a little hard to interpret; what is the advantage of a bagged model vs the plain trees? Are you using a simple majority vote for combination? What are the variances between the different bootstraps? Second - what do you mean by 95% ? Do you mean that out of 99999 good parts 4999 are thrown away? and one bad one is picked out as bad ? Third - what…
I'd hazard to guess that the 95% is the reduction in how many parts made it through the first test only to be caught later at the more expensive stage. So instead of binning 100 parts a month at that second stage, they now bin 5 parts a month and catch way more early on.
It sounds like the OP is using ML to identify flaws that simply just occur due to imperfections in the manufacturing process. That's life, it happens. You can know that they will occur without necessarily being able to prevent them because maybe there's some dust or other particulates in the air that deposit into the resin occasionally, or maybe the resin begins to cure and leaves hard spots that form bond flaws. There's heaps of possible reasons. It sounds more like the ML is doing classification of 'this too much of a flaw in a local zone' vs 'this has some flaws but it's still good enough to pass', which is how we operate with casting defects. For example, castings have these things called SCRATA comparitor plates, where you literally look at an 'example' tactile plate, look at your cast item, then mentally decide on a purely qualtative aspect which grade of plate it matches. Here [1] are some bad black and white photos of the plates.
[1] http://www.iron-foundry.com/ASTM-A802-steel-castings-surface...
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Yes, thanks for highlighting the deficiency in my original post. I can see it is easily interpretated as you did. I added a clarification (or what I hope is one).
I know a few companies riding the ml/nn train. Any chance you are based in NYC?
I work in manufacturing. We have an acoustic microscope that scans parts with the goal of identifying internal defects (typically particulate trapped in epoxy bonds). It's pretty hard to define what size/shape/position/number of particles is worthy of failing the device. Our final product test can tell us what product is "good" and "bad" based on electrical measurements, but that test can't be applied at the stage of…
This sounds like a pretty standard use of ML to me. No need to feel guilty, this stuff just isn't very difficult from a user's perspective, especially if you use the right libraries. It helps if you maintain a good bookkeeping of your experiments, so you have a good picture of what works and what doesn't. By the way, control engineering for industry used to be very difficult (but is paid very well), and requires know…
Earlier quoted context omitted.
Ok there are some warning signs here. First, bagged decision trees are a little hard to interpret; what is the advantage of a bagged model vs the plain trees? Are you using a simple majority vote for combination? What are the variances between the different bootstraps? Second - what do you mean by 95% ? Do you mean that out of 99999 good parts 4999 are thrown away? and one bad one is picked out as bad ? Third - what…
It sounds like the OP is scanning for internal defects in bonds via impurities being trapped in there. These occur relatively randomly and there's some balancing point where it's just not worth trying to make the production line cleaner vs binning parts that fail some QA criteria. I do similar things with castings, where you simply just get certain voids and porosity in the steel when cast and either you can spend a…
To clarify on that 95% value because it is admittedly really vague: That's actually a 95% correct prediction rate. So far we get ~2.5% false-positives and ~2.5% false-negatives. 2.5% of the parts evaluated will be incorrectly allowed to continue and will subsequently fail downstream testing (no big deal). More importantly, 2.5% of parts evaluated will be wrongly identified as scrap by the model and tossed, but this still works out to be a massive cost savings because a lot of expensive material/labor is committed to the device before the downstream test.
The entire product I built over the last year can be reduced to basic statistics (e.g. ratios, probabilities) but because of the hype train we build "models" and "predict" certain outcomes over a data set. One of the products the company I work for sells more or less attempts to find duplicate entries in a large, unclean data set with "machine learning." The value added isn't in the use of ML techniques itself, it's…
It's interesting to me that with all the ML hype, it's still not clear what constitutes ML. A basic k-means or naive Bayes approach will show up in ML textbooks, but those aren't clearly different from "use some statistics to make a prediction". There's an interesting group of marginal approaches that have existed as-is for years, but have increasingly focused their branding on machine learning as its profile has ris…
Ha, sounds like a classification problem! Let's use ML to find the boundary.
From Coursera - we use ML in a few places: 1. Course Recommendations. We use low rank matrix factorization approaches to do recommendations, and are also looking into integrating other information sources (such as your career goals). 2. Search. Results are relevance ranked based on a variety of signals from popularity to learner preferences. 3. Learning. There's a lot of untapped potential here. We have done some res…
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So is this like the Amazon "feature" where I buy a coffee table on Amazon, then I get suggested to buy a coffee table EVERY DAY for 3 months. Literally row after row of coffee table? Because there must be a big pool of people who buy 1 coffee table buying more coffee tables immediately after?
It's a hard problem to determine the repeat purchase cadence of a product. At one end of the bell curve you have items re-purchased frequently, e.g. diapers or grocery, and on the other end you have items that are rarely repurchased. I haven't looked at coffee tables specifically, but I know when I've looked at home products in the past I've been surprised at how frequently people will buy two large items, e.g. TVs o…
This topic must be extremely interesting (good suggestions could increase sales by a LOT) and smart people must have been working on it for quite a while.
- What is the fundamental reason why this is a hard problem?
- What's up with the coffee tables specifically, could you, for the hell of it, look into that category and tell us what the actual related products are? Let us (fail to) guess how these products are related, but don't let us hanging :-)
Earlier quoted context omitted.
It sounds like the OP is scanning for internal defects in bonds via impurities being trapped in there. These occur relatively randomly and there's some balancing point where it's just not worth trying to make the production line cleaner vs binning parts that fail some QA criteria. I do similar things with castings, where you simply just get certain voids and porosity in the steel when cast and either you can spend a…
This is pretty spot on. We know why the defects happen and why they cause downstream test failures, but we lack the ability to prevent (all of) them. To clarify on that 95% value because it is admittedly really vague: That's actually a 95% correct prediction rate. So far we get ~2.5% false-positives and ~2.5% false-negatives. 2.5% of the parts evaluated will be incorrectly allowed to continue and will subsequently fa…