Our cousin Jaime has put on a little weight recently, and as we sit across from him at a family dinner, we can’t help but notice that his favorite shirt is stretching apart at the seams trying to accommodate his expanding girth. We snicker and tell him it looks like his body is overfitting his clothes. He snickers back and tells us we used the word “overfitting” incorrectly.
He’s right, we did. “Overfitting” is actually what happens when we create a model based on data that can’t be extrapolated to the real world. It can’t predict; it can only draw conclusions based on the historical data upon which we built it. And if that data—our training data—isn’t reflective of reality, then our model won’t be, either. This usually happens as a result of one of two things: either we have way too many data parameters, or we have so few data points that our model treats the anomalies as part of the pattern it’s supposed to recognize and predict.
As an example, let’s get back to poor Jaime. Let’s say we’re trying to predict how many cans of Pepsi he’ll drink in a given day, so we keep a log for seven days. On Monday, Tuesday, and Thursday, he drank four cans. On Wednesday and Saturday, it was five. Friday was three, and Sunday was zero. Based solely on this data, our model might predict that Jaime won’t drink Pepsi on Sundays. In reality, though, he was sick that day and only drank green tea with honey and milk. That anomaly should not be a part of our Pepsi predictions, because it was just that: an anomaly. Our model overfits the data.
Related or Semi-related Video
Finance: What are Financial Projections?96 Views
Finance allah shmoop what Our financial projections Well they're guesses
okay educated guesses we presume data is assembled reviewed and
then a crystal ball is gazed into And while projections
are made in the case of mature companies with long
cycle businesses the projections are often extremely educated and accurate
Yeah think about boeing's projected jet engine sales Well the
company takes three to four years just to set up
a fabrication run of a new style of engine and
they're working with rolls royce to do it Rolls royce
makes jet engines along with cars Their orders come in
and while they were partly paid for already and the
company knows it will produce somewhere between two hundred two
hundred fifty engines next year two hundred fifty two three
hundred engines following here and three hundred three hundred fifty
engines the year after that And yes there is some
variability and revenues But if the company produces fewer engines
well they'll have fewer costs as well So the range
they predict for profitability is pretty narrow That is they'll
have operating profit somewhere between eighty and ninety million bucks
each year Going up a little bit the next few
years That's The financial projection anyway can a bomb go
off in the factory or rather someone is smoking around
the fuel depot and then you know glam Yep that
can happen Is it likely No but it can And
it would really throw off the projections Right So projection
Is just a projection it's not a guarantee it's a
guess with other companies while like much younger ones projections
are well way more of a guess At the other
end of the rainbow from the boeing rolls royce jet
engine thing there's a brand new company just called rolls
it's an e bike company with assisted pedaling so that
riders feel like they're somewhere between superman and lance armstrong
You know the steroid guy company has had early success
having sold two thousand bike models off their website for
two grand each they're about to raise twenty million dollars
in venture capital funding Tio go big so now the
projections get really vague Will the company sell a million
bikes in three years Well that's what the founders think
but they're enthused and young for the company only sell
ten thousand bikes in three years Sure if they meet
the former projection they're billionaires If they meet the ladder
they're bankrupt Either way they can project with certainty that
their outcome begins with a b generally speaking the more
mature the industry and product the narrower or more accurate
the financial projection with startups who have no history Well
one never knows if they're projecting growth in the future 00:02:43.065 --> [endTime] like google or shmoop Come on
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