Am I supposed to understand my training dataset for a machine learning project? TL;DR: I am expected to work on a dataset with missing/wrong information about measurement units. Is this a common practice?



Longer version:

I am studying a Masters's program composed mostly of machine learning. This semester I need to complete a 12 credit Project Module. Project Module means that there are no lectures, just a problem formulation, consultations with a teacher, and then a final report, source code and presentation are submitted.

The problem is to identify rocks by melting them with a laser and measuring the spectrum of the resulting plasma.

The training data I have is a bunch of measured spectra (example visualization). Each file comprises of comma separated tuples (wevelength, intensity). 

My teacher didn't have the time to figure out what is being measured when we say "intensity" and sent me the "example" above to figure for myself. Well, I figured it's radiance, based on information of the same source as the example I was sent(here)

Now, radiance is measured in watt divided by solid angle and surface area. This is strictly non-negative. The example graph also cuts the ordinate at 0.

Yet when I look at the provided dataset, I can identify regions such as
this one:

 188.5999999999995,15.175680381419365
 188.6999999999995,0.872615069518224
 188.7999999999995,-8.345751621890303
 188.8999999999995,-10.279252960079765
 188.9999999999995,-4.22895372957017
 189.09999999999948,3.978827692507486
 189.19999999999948,16.603400130260713
 189.29999999999947,-12.791825667786197
 189.39999999999947,-4.859145351410255
 189.49999999999946,4.452390240582065
 189.59999999999945,-14.106010857508583
 189.69999999999945,7.039306431256487
 189.79999999999944,-7.3039003395809505


Negative line intensities with values far from 0. 

And here is my question. Both my teacher and a couple of more knowledgeable colleagues agree that there is nothing wrong with this:


  The data you have is real world data collected with a hand held. Your model should be able to deal with the data as it is. You can apply preprocessing steps if you want to avoid negative data values.


To me it is absurd to even consider a dataset without first sanity checking it. Just as unacceptable I would call working with a preprocessed dataset without knowing how was it preprocessed (maybe I am seeing centered values, but no one told me the mean; maybe I am seeing a logarithmic scale).

Is it customary for a task to be presented this way?
From the point of pure exercise in machine learning, you do not care whether the data is reliable and whether it was collected or taken from the ceiling. Your task is just to design a recognition algorithm that classifies the objects into groups based on the numbers associated with them. If you can do it, you pass, if not, you fail.

From the perspective of practical recognition of rocks, your algorithm (if it works) will be useful only if you trained it on accurate data or data with not too much noise and if you know what the numbers mean, so if you later are given the information in different units or on an altogether different scale (say, logarithmic instead of linear), you'll know how to convert before feeding them into the machine. If your algorithm is intended for real use, the people who requested the work should be most interested in getting things right, so you should be able to ask them as many relevant questions as you want. If it is just a practice problem, just forget about "rocks", "radiance", etc. View all that as an abstract classification problem about objects and numbers without any meaning whatsoever.

From what you are saying, it looks like you are in the first case scenario. I agree that the exercise is about as meaningful as the exercise in computing the area of the triangle with sides 6,7,8 and the altitude of length 5 drawn to the side of length 7. My daughter really had that as a problem on one of her exams and her teacher replied to my objections that at that stage of learning he just wanted to make sure that kids knew which side to multiply by the height. He wasn't a bad math. teacher overall, by the way, just a bit reckless about the details that weren't directly related to the current topic. Apparently, your professors have a similar attitude.