MAPE can be a problem also if you have a problem where rare excursions are what you want to predict and the cost of missing an event is much higher than predicting a non-event. A model that just predicts no change would have very low MAPE because most of the time nothing happens. When the event happens, however, the error of predicting status quo ante is much worse than small baseline errors.
LA-SF is nearly 500 MILES. That's close to 800km and driving through LA makes it feel like 1000km. If the sleeper would extend on to San Diego, this would be a sweet item for me.
One kind emits light through a folded optic path through the test gas and detects the difference in the absorption between wavelengths of light are used. The difference in absorption is minute so the optical path needs to be as long as possible which makes it hard to make the detector small.
The other kind detects the sound emitted when a precise frequency of IR is transmitted through a gas. If the frequency of the light is just right, it will be absorbed by the CO2 causing it to heat up ever so slightly which causes a tiny bit of sound.
Both of these are incredibly ticklish devices to design and build in mass.
That you can get these for as low as about $20 in quantity is actually mind bogglingly cheap.
You're conflating determination of factual and legal questions (out of scope) with modeling the decision tree (in scope and useful).
The function you ask about would be "getDeductiblePercentage()," and the unit tests would return various hard coded numbers. Actually determining that value for a real taxpayer is still hard.
Being able to show how information flows through the US tax code would be useful, even if it doesn't solve all the problems that arise from its intricacy.
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