Why squared error? Because you can solve the equation to minimize squared error using linear algebra in closed form.
Why L2 regularization? Same reason. A closed form solution exists from linear algebra.
But at the end of the day, you are most interested in the expectation value of the coefficient and minimizing the squared error gives you E[coeffs] which is the mean of the coefficients.
I cannot speak for eanzenberg but I think his comment was less about his personal justification and more about the rationalizations that have been used in the history of stats.
Gauss quite openly admitted that the choice was borne out of convenience. The justification using Normal or Gaussian distribution came later and the Gauss Markov result on conditional distribution came even later.
Even at that time when Gauss proposed the loss, it was noted by many of Gauss' peers and (perhaps by Gauss himself) that other loss functions seem more appropriate if one goes by empirical performance, in particular the L1 distance.
Now that we have the compute power to deal with L1 it has come back with a vengeance and people have been researching its properties with renewed almost earnest. In fact there is a veritable revolution that's going on right now in the ML and stats world around it.
Just as optimizing the squared loss gives you conditional expectation, minimizing the L1 error gives you conditional median. The latter is to be preferred when the distribution has a fat tail, or is corrupted by outliers. This knowledge is no where close to being new. Gauss's peers knew this.
3 times yes: "The latter is to be preferred when the distribution [...] is corrupted by outliers."
I am working in chemoinformatics, the main methods used by the academics to regress parameters have not changed in the past 40 years even so we went from small carefully assessed data sets (think 200 experimental points) to larger (10000, sometimes millions) with a lot of outliers from data entry errors, experimental errors, etc.
The end results is that when I see models of interest without the raw data, I reregress the parameters using my own datasets because most of the time you can barely trust them (even if coming from well known research centres).
> Gauss quite openly admitted that the choice was borne out of convenience.
That's quite interesting. Do you have a reference for that?
From my understanding, the popularity of the least squares method came (at least in part) from Gauss' successful prediction of the position of Ceres. Was this just because people not using least squares were not able to calculate it?
It's in the original paper in which he derives the normal distribution. Well worth a read. I last had a copy of it in the fourth basement down in the university library about fifteen years ago - it might be still there.
Not disagreeing with your points about L1 but I want to point out that you can also do things to make L2 more robust to outliers (and have better empirical performace), such as winsorizing the data.
I think just historically it's interesting. Every statistician was using OLS before computers because they could solve it with pen and paper, so when computers came out it was ported over. But with computers you can minimize any loss function.
However it is useful to have a closed form solution because it guarantees you actually minimized it. Other strategies to minimize functions don't guarantee that but they're still extremely useful.
There is a Kaggle competition right now that uses mean absolute error, and this makes the problem substantially harder. For a practical discussion of techniques used to solve machine learning problems that use mae see the forums in: https://www.kaggle.com/c/allstate-claims-severity/forums
As touched upon in the article, the objective not being differentiable is a big deal for modern machine learning methods.
Mean absolute error is differentiable almost everywhere. Having objectives that are not differentiable, but are differentiable almost everywhere is very common - in a deep net, if you have rectified linear activations (very common) or L1 regularisation (not unheard of), you have an objective that is not differentiable everywhere ... but the methods still work.
This is the difference between practice and theory. In theory differential objectives don't matter, in practice for medium to large datasets they make machine learning a lot faster. Speed is critical, as you need to be able to iterate quickly. The solution most commonly used on Kaggle is to transform the target feature and then minimize mean squared error, but there is some systematic uncertainty introduced by this.
> As touched upon in the article, the objective not being differentiable is a big deal for modern machine learning methods.
I'm not sure the absolute value is a big problem here. You still get a convex optimization problem. In neural networks a lot of people use ReLU or step activations functions, which are no more differentiable than the absolute value.
For minimizing the square of the errors I think the good reason is because, assuming your data has gaussian probability distribution, minimizing the square error corresponds to maximizing the likelihood of the measurement, as you and others have said.
Why do we assume gaussian errors? There is seldom a gaussian distribution in the real world usually because the probability for large error values doesn't not decay that fast. We use it because the math is easy and we can actually solve the problem assuming that.
Yes, sort of. But I think he says a lot of unnecessary things not getting at the root of the issue.
I left out some detail I should have said, like what is so special about a gaussian that makes the math easy. So I will say it.
A measurement can infer a probability distribution for what the measured quantity is. A second measurement, on its own, also infers some probability distribution for what the measured quantity is. It we consider both measurements together, we get yet another probability distribution for what the measured quantity is. The magic is that if we had a gaussian distribution for the measurements, then the distribution for the combined measurements is also a gaussian. This is not true in general. As long as we have gaussian distributions we can do all the operations we want and the probability distributions are gaussian and can be fully described by a center point and a width. (Forgive me for the liberties I am taking here.) The basic alternative to exactly solving the problem is to actually try to carry around the probability distribution functions, which is not practical even with very powerful computers.
"if the best model is not differentiable, you should still use it."
I'm not sure I would say that - neural nets are "near everywhere differentiable", for example. Without differentiability we're stuck with, for example, discrete GAs for optimization, and you can throw all your intuition out the window (not to mention training/learning efficiency).
A few misconceptions I should correct in this comment.
- There is plenty of existing technology for handling non-differentiable function. Functions like the absolute value, 2-norm, and so on have a generalization of the gradient (the subgradient) which can be used in lieu of the gradient.
- That functions are "almost everywhere differentiable" (i.e. the non-differentability lies in a manifold of zero measure) makes these functions behave pretty much like smooth ones. This is often not the case as optima often conspire to lie exactly on these nonsmooth manifolds.
Yes, that was what I was saying. Absolute value, 2-norm are fine thanks to subgradient techniques and theory, as well as their differentiability over the majority of the function - but you can imagine tons of non-differentiable models where the subgradient is mostly useless and we generally use convex relaxations or other smoother analogs.
The fact that squared error is differentiable is not irrelevant. You can solve some machine learning models faster with differentiable objectives (most notably xgboost). Speed is important, you need to optimize your models and the longer it takes to run a model the less things you can try.
Regardless of how distributed the errors are, the squared error fit will provide the expectation value of the variable, which is the mean. It will say nothing of the error of the mean it calculates.
We want a metric essentially because
if we converge or have a good
approximation in the metric
then we are close in some important
respects.
Squared error, then, gives one such
metric.
But for some given data, usually there are several metrics we might use, e.g., absolute error (L^1), worst case error (L^infinity), L^p for positive integer p, etc.
From 50,000 feet up, the reason for using squared error is that get to have the Pythagorean theorem, and, more generally, get to work in a Hilbert space, a relatively nice place to be, e.g., we also get to work with angles from inner products, correlations, and covariances -- we get cosines and a version of the law of cosines. E.g., we get to do orthogonal
projections which give us minimum
squared error.
With Hilbert space, commonly we
can write the total error
as a sum of contributions
from orthogonal
components, that is, decompose
the error into contributions
from those components -- nice.
The Hilbert space we get from squared error gives us the nicest version of Fourier theory, that is, orthogonal
representation and decomposition,
best squared error approximation.
We also like Fourier theory with
squared error
because
of how it gives us the Heisenberg
uncertainty principle.
Under meager assumptions, for
real valued random variables
X and Y, E[Y|X], a function of X,
is the best
squared error approximation
of Y by a function of X.
Squared error gives us variance, and
in statistics sample mean and variance are sufficient statistics for the Gaussian; that is, for statistics, for Gaussian data, can take the sample mean and sample variance, throw away the rest of the data, and do just as well.
For more, convergence in squared error can imply convergence almost surely at least for a subsequence.
Then there is the Hilbert space result, every nonempty, closed, convex subset has a unique element of minimum norm (from squared error) -- nice.
Ah but square error is not a metric, its square root is a metric.
Many nice properties of the square loss (in fact un-fucking-believably nice properties) stem not from the fact that its square root is a metric but from the fact that it is a Bregman divergence. Another oft used 'divergence' in this class is KL divergence or cross-entropy.
Bregman introduced this class purely as a machinery to solve convex optimization problems. His motivation was to generalize the method of alternating projection to spaces other than a Hilbert space. But it so turned out that Bregman divergences are intimately connected with the exponential family class of distributions, also called the Pitman, Darmois, Koppman class of distribution. It takes some wracking of the brain to come up with a parametric family that does not belong in this class if one is caught unprepared, almost all parametric families used in stats (barring a few) belong to this class.
One may again ask why is this class so popular in probability and statistics, the answer is again convenience, they are almost as easy as Gaussians to work with, they have well behaved sufficient statistics, and their stochastic completion gives you the entire space 'regular' enough distributions with finite dimensional parameterizations.
You mentioned conditional expectation. So one may ask what are the loss functions that are minimized by conditional expectation. Bregman divergences are that entire class. Of course square loss satisfies it too (more importantly L2 metric on its own does not, it is the act of squaring it which does this).
I am not aware of a book that talks about these things. Its spread across several research publications. Some of the older ones in Russian, but translations are available.
product space. Two examples are the real
line and the 3 space we live in (at least
locally ignoring general relativity).
But the biggie points about the Hilbert
space definition are (A) the other,
somewhat astounding (vector space),
examples and (B) how much can do in
Hilbert space that is close to good old
high school plane (2D) and solid (3D)
geometry and close to good, old freshman
calculus with limits, convergence, etc.
So, in particular, in Hilbert space we get
the Pythagorean theorem of plane geometry
and perpendicular projections as shortest
distance from a point to a plane in solid
geometry -- two biggies for pure/applied
math. We get the triangle inequality.
With the inner product, we get angles and
orthogonality. And the core data we need
for projections are just some inner
products.
The most important examples of a Hilbert
space are, for positive integer n, the set
of real numbers R and the set of complex
numbers C, the vector spaces of linear
algebra R^n and C^n -- e.g., C^n is the
set of all n-tuples of real numbers. Then
as in whatever first course you had that
talked about vectors and dot (inner,
scalar) products, with those inner
products R^n and C^n are Hilbert spaces.
The part about complete is a
generalization of completeness in the real
numbers, that is, the biggie way the real
numbers are better than just the rational
numbers. In short, for example,
intuitively, in the real numbers, if a
sequence appears to converge, then there
really is a real number there for it to
converge to. Of course, that's not true
in the rational numbers since, e.g., can
have a sequence of rational numbers that
converges to the square root of 2 but
doesn't really converge in the rational
numbers because square of 2 is not a
rational number. This stuff about
"appears to converge" is called Cauchy
convergence and is a weak definition of
convergence. The point about completeness
is that Cauchy convergent sequences really
are convergent, that is, have something to
converge to and do converge to it
(essentially in the sense of limits you
saw in calculus or high school algebra).
If are taking limits to define or
approximate what really want, then also
really want completeness so that what
converge to exists. So, that's
completeness -- for a Hilbert space, we
insist on that.
Of course, need a background in linear
algebra. So, for a first book, get any of
the popular ones. If you wish,
concentrate on the more geometrical
parts and do less on the algebraic parts
-- e.g., if there is a chapter on group
representation theory, Galois theory,
linear algebra over finite fields, where
things go wrong when the field is the
rationals, or algebraic coding theory,
then feel free to leave that material for
later. Likely should pay attention to
dual spaces, but if wish can go light on
adjoint transformations since they are
less interesting when have an inner
product. Curiously, can go light on
change of basis for linear
transformations, that is, the difference
between vectors and coordinate vectors.
Concentrate on dimension, linear
independence, linear transformations,
maybe touch on quotient spaces, use Gauss
elimination as a good example of such
things, eigenvalues and eigenvectors,
orthogonality, and the polar decomposition
(the core of factor analysis, singular
value decomposition, matrix condition
number, and more). If there is a little
on the associated numerical analysis, then
go ahead -- e.g., learn to accumulate
inner products in double precision --
sure, take 10 minutes to see how to add
iterative improvement to Gauss elimination
and matrix inversion. For
pseudo-inverses, cute material, but,
especially for your question, likely won't
see it again and can skip it.
Then if have some time, take a fast pass
through the classic, Halmos, Finite
Dimensional Vector Spaces. It was
written in 1942 when Halmos had just
gotten his Ph.D. under J. Doob (e.g., as
in Stochastic Processes and, more
recently, Classical Potential Theory and
Its Probabilistic Counterpart) and showed
up at the Princeton Institute of Advanced
Study and asked to be an assistant to John
von Neumann, likely the inventor of
Hilbert space. Well, Halmos wrote his
book to be a finite dimensional version of
linear algebra as if it were Hilbert
space, which is commonly infinite
dimensional. So you get a gentle
introduction to Hilbert space. You get
good at eigenvalues and eigenvectors, the
polar decomposition, orthogonality,
transformations that preserve distances
and angles, etc. You get a lot of
geometric intuition.
BTW, at one time, Harvard's Math 55 used
Halmos, Baby Rudin (below), and a book by
Spivak as the three main references.
Get a start on probability and statistics.
A college junior level course should be
sufficient. Don't take the course too
seriously since will redo all the good
parts from a much better foundation soon!
Note: Elementary stat courses common get
all wound up about probability
distributions. Well, they do exist, are
at the core of probability theory, and are
very important, both in theory and
conceptually, but, actually, in practice
usually they require more data than you
will likely have, especially in dimensions
above 1. So, in practice, mostly can't
actually see the actual data of the
distributions you are working with! You
should hear about the uniform,
exponential, chi-squared, and Gaussian and
go light on the rest. The Gaussian is
profound and won't go away even in
practice although is less important in
practice than long assumed in, say,
educational statistics.
Then take a good pass through at least the
first parts of Rudin, Principles of
Mathematical Analysis, AKA Baby Rudin.
For the exterior algebra in the back of
the more recent editions, well, likely get
that elsewhere, say, now in English,
directly from Cartan. The first parts of
Baby Rudin cover metric spaces well
enough. So, in Baby Rudin, get good at
working with the limits, completeness
property, compactness, etc. of
mathematical analysis, that is, not
algebra, geometry, topology, or
foundations, although the metric space
material is the same as part of the part
of topology called point set topology.
Then learn more in mathematical analysis,
in particular, measure theory. Measure
theory essentially replaces the Riemann
integral you learned in calculus and Baby
Rudin. 'Bout time! Net, measure theory
is a slightly different way to use limits
to define the integral (areas, volumes,
etc.) -- the first, biggie difference is
that do the partitioning on the Y axis
instead of on the X axis. The biggie
reason: The resulting integral easily
handles the pathological cases, especially
involving limits, that the Riemann
integral struggles with. Don't worry:
The integral of, say, x^2 over [0,1] is
still the same, IIRC, 1/3rd, right? But
consider the function f: [0,1] --> R
where f(x) = 1 if x is rational and 0
otherwise. Then the Riemann integral of f
over [0,1] does not exist, but the measure
theory integral does and gives 0 for the
result.
There are at least two now classic books,
Royden, Real Analysis and Rudin, Real
and Complex Analysis. But there are
more, and likely more can be written. For
Rudin, can f'get about the last half on
functions of a complex variable.
Royden is easier to read. Rudin has the
math more succinctly presented. Some
people believe that Rudin is a bit too
severe for a first version; but if get
used to how Rudin writes, he's really
good.
There in Rudin get good introductions to
Banach space (a complete, normed linear
space, that is, assume a little less than
for a Hilbert space) with a few really
surprising theorems, Hilbert space with an
isomorphic argument that they are really
all the same, a really nice chapter on
Fourier theory (Baby Rudin does Fourier
series; R&CA does the Fourier integral),
and some nice applications.
With that background in measure theory,
then take your good pass through
probability. So, probability becomes a
measure as in measure theory except the
values are always real and in [0.1]. A
random variable finally gets a solid,
mathematical definition -- it's just a
measurable function (measurable is very
general; in practice and even in nearly
all of theory, essentially every function
is measurable; in the usual contexts, it
takes some cleverness to think of a
function that is not measurable). And
expectation is just the measure theory
integral (with meager assumptions).
And in probability get some assumptions
don't see in measure theory --
independence and conditional independence,
and these two yield wonders nothing like
in just measure theory.
Go somewhere; look at something; get a
number; then that's the value of a random
variable. In practice, suppose there are
20 random variables, you have numerical
values for 19 of them, and you can argue
that those 19 are not independent of the
20th and that you have some data on how
they are dependent, then you have a shot
at estimating the 20th. Presto, bingo,
get a rich as James Simons, do machine
learning, get big houses, Cadillacs,
Ferraris, a yacht, a private jet -- maybe!
Also get to use the Radon-Nikodym theorem
(proved in both Royden and Rudin, and in
Rudin with von Neumann's cute proof) for
the grown up version of conditioning
(i.e., Bayesian) and, from there, in
stochastic processes, Markov processes and
martingales (astounding results).
Books include L. Breiman, Probability,
K. Chung, A Course in Probability
Theory, J. Neveu, Mathematical
Foundations of the Calculus of
Probability. And there are more. IIRC
Breiman and Neveu were both students of M.
Loeve at Berkeley -- sure, can also get
Loeve's Probability in two volumes. Of
these, Neveu is my favorite; it's elegant;
but for most readers it is too succinct.
Hilbert space again? It turns out, the
set of all real valued random variables X
such that E[X^2] is finite is a Hilbert
space. Yes, completeness holds; with some
thought, that result seems astounding,
like there is no way it could be true, but
it is.
Now just derive grown up versions of most
of the main results of elementary
statistics for yourself from what you have
learned. E.g., for the Neyman-Pearson
result on most powerful hypothesis
testing, just use the Hahn decomposition
from the Radon-Nikodym theorem.
And, with the Radon-Nikodym theorem, get a
grown up version of sufficient statistics,
right, based on a classic paper by Halmos
and Savage.
Along the way will notice that, the last
time I looked, Baby Rudin defined the
Riemann integral on closed intervals of
finite length, but right away probability
and statistics want to integrate on the
whole real line, the whole plane, etc., do
change of variable manipulations with such
integrals, etc. Well, for the
prerequisites, those are in measure theory
where the first version of its integral
applies also on the whole real line, the
whole plane, and much more. Measure
theory also give you the clean, powerful
versions of differentiation under the
integral sign and interchange or order of
integration.
Ah, why bother to teach the Riemann
integral at all? :-)
I have a utilitarian understanding of mathematics (vector spaces, SVD, orthogonality, invariances, etc.) and over time appreciating the underlying characteristics/relationships, which I recently got a taste of from T. Wickens, The Geometry of Multivariate Statistics.
I look forward to understanding measure theory and related math.
I am no math expert, but I have always thought about it like this. The squared error is like weighting the error by the error. This causes one big error to be more significant than many small errors, which is usually what you want. Am I on the right track?
I just thought it was to give positive and negative error values the same treatment. Moreover I think that it's debatable that one big error is more important than many small errors. That is conceivably a bad strategy, in some cases -- if most points have low error, do you really want to penalize your candidate function for having a very few bad outliers? To me that is no better than giving extra favor to a few points that happen to have low error.
No, that's exactly why absolute error is better. "Big errors" are called outliers, they're (relatively) rare, often caused by bad data (measurement errors, typos, etc.) and substiantially influence the outcome of your calculation. In other words, squared error is less robust.
But squared error is easier to compute. So, in practice, what you do is you remove outliers (e.g. cap the data at +-3sigma) then use squared error.
> So, in practice, what you do is you remove outliers (e.g. cap the data at +-3sigma) then use squared error.
But if you are say fitting a function to the data, you can't tell beforehand which data-points are the outliers. So in that case perhaps you need an iterative approach of removing them (?)
Squared error represents the underlying belief that errors in various dimensions, or errors in independent samples, are linearly independent. So they add together like orthogonal vectors, forming a vector whose length is the square root of the sum of the squares. Minimizing the square error is a way of minimizing that square root without the superfluous operation of calculating it.
It's fine to list some reasons for using squared error, but you really can't decide on the error function without referring to a problem you're trying to solve.
Just look at the success of compressed sensing, based on taking the absolute value error seriously.
"inner products/gaussians" - the absolute value (and also cuberoot of absolute cubes, fourth root of fourth powers) also define inner products. Likewise, there are "gaussian-like formulas" which take these powers instead of squared.
However: if you look at the shape of the squareroot of sum squares, it's a circle, so you can rotate it. If you take the absolute, it's a square, so that cannot be rotated; the cuberoot of cubes and fourthroot of fourths, etc. look like rounded edge squares, and that cannot be rotated either, so if you have a change of vector basis, you're out of luck.
With the gaussian forms of other powers, none of them have the central limit property.
What kind of inner products are defined by the absolute value, cuberoot of absolute cubes, fourth root of fourth powers? I never heard of that and would be glad to learn about it.
You may find it interesting to read about Lp norms, and their relationship to inner products on vector spaces. I think the OP is mixing up norm and inner product terminology. This happens often because you derive an norm from any inner product, but the other way may not exist.
If you plot on the plane the distance = 1 line, then L_1 gives you a diamond, L_2 a circle, L_inf a square. [More precisely, the unit circle under the related metric (distance function) looks like those euclidean shapes]
They don't give inner products, but they do give norms. But inner products are, in some ways, more convenient than general norms, hence squared error as opposed to other things. It's not that squared error is necessarily what you fundamentally care about; it just happens to be so conveniently analyzed, because the mathematics of inner products is convenient.
hah whoops! I did confuse inner products with norms. But it is true that the L2 norm is the only one that survives transformations to arbitrary unit basis vectors.
My explanation for squared error in linear approximation always was: because it minimizes the thickness of the line that passes through all the data points.
(Per the old math joke - you can make a line passing through any three points on a plane if you make it thick enough.)
Or why use variances when there are standard deviations (the square root of the variance) which have more easily interpreted units? One commonly cited reason is that one can sum variances from different factors, which one cannot do with standard deviations. There are other properties of variances which make them more suitable for continued calculations[1]. This is why, for instance, variances are often utilized in automated optimization packages.
Interesting discussion. Not sure about the breakdown between ridge regression and LASSO though. The difference is not in the error term but in the regularization term.
Squared error because the uncertainties in independent, normally distributed random variables add in quadrature. I expect that this could be proved geometrically using Pythagoras's theorem, so in that sense the comments about orthogonal axes are vaguely on the right track.
Normally distributed variables because the central limit theorem.
Why not KL-Divergence, which measures the error between a target distribution and the current distribution? From the perspective of Information Theory, it is the best error measurement.
Oh, and let's not forget that for a lot of problems minimizing the KL-divergence is the exact same operation as maximizing the likelihood function.
To give just a taste for the nice properties of KL, if you are using a layer 1 NN with the sigmoid function as the transform, using square loss gives you an explosion of local minima. OTOH using KL in its place would have given you none. Numerically accuracy is pretty much a non-issue, people have known how to handle KL numerically since the last 40 or so years.
BTW using KL on equivariant Gaussian gives you square loss, apparently the loss you prefer.
Another pro tip - absolute error magnitude is the convex hull of non-zero entry count for vectors (l_0 norm in some circles). So in the convex minimization context (and for most other smooth loss terms in general) you end up with solutions with more zero entries and few possibly large non-zero entries.
I noticed this got voted up and down more than usual. Maybe a little elaboration:
Square often corresponds to power/energy in systems AND energy (integral of power) is preserved. That relationship between physics and math allows a lot of useful transformations.
Because it's very useful for symmetric distributions. If the distribution is highly non-symmetric, then the mean != maximum likelihood, which is probabilistic.
What is a problem geometric mean solve? That was my question the entire time.
When people ask "why machine learning?" the answers are "machine learning can do these things blablabla", not "you must specify the problem you're trying to solve".
It gives you a way to average together two things that have units that have nothing to do with each other and then compare two such averages and have the comparison make sense, as long as your units were consistent.
As a silly example, say you want to average 1kg and 1m and compare that average to the average 2kg and 0.5m. With arithmetic mean, ignoring the fact that it's nonsense to add different units, you could get numbers like (1+1)/2 = 1 and (2+0.5)/2 = 1.25 if you use kg and m, but numbers like (1 + 100)/2 = 50.5 and (2 + 50)/2 = 26 if you use kg and cm. Notice that which one is bigger depends on your choice of units. On the other hand, the geometric mean of the two examples is always the same as long as you use consistent units: 1 for both if you use kg and m, and 10 for both if you use kg and cm.
In practice, this sort of operation is only useful if you have multiple measures of some sort along different axes (think performance on 3 different performance tests) and you're being forced to produce a single average number. Again, a fairly silly thing to do, but _very_ common: just about every single performance benchmark does this.
I have stock position which changed in value by a factor of 1.10 2007, by a factor of 0.80 2008 by a factor of 1.15 2009. Is there any sort of representative number x for how much the value changed per year?
The final value of my stock position should come out the same when using x for every year, i.e. x * x * x = 1.10 * 0.80 * 1.15
That's not correct. Even though the magnitudes of the value in isolation shrinks, the relative magnitudes are still amplified which is what matters.
Consider values 1/2 and 1/4: in the original space it's double but in the squared space it becomes 1/4 and 1/16 so the difference is 4x. Also relevantly if you compare eg 0.9 and 1, the gap between them is amplified after squaring.
An honest question - do we even need statistics when we have machine learning? Statistics to me appears as a hack/aggregation of data we couldn't process at once in the past; these days ML + Big Data can achieve that and instead of statistics we can do computational inference instead. To me this looks like looking back to "old ways" for a reference point instead of looking forward to the unknown but more exciting.
Sorry you're getting down-voted, I don't think it's an unreasonable question.
In the sense I think you're using it, "statistics" are really methods for dimensionality reduction - we take means, and medians and standard deviations with the hopes that they will capture the parts of the data we care about. This is important for two reasons - for one, for anything even moderately high dimension we'll never have enough data to be able to forego some means of aggregation due to the "curse of dimensionality". Secondly, the human-machine interaction information bandwidth is annoyingly low, so we need some way to compress any information for human consumption. "Statistics" are one way we do so.
"Statistics" is also a field of study based around understanding how multiple data points relate to each other - that is of course critical to machine learning, and I think the terminology collision is why you're getting downvoted.
Machine learning is often considered a statistical technique. The main difference seems to be that in traditional statistics, people derive practice from theory, whereas in ML people will try out techniques and figure out the theory later. That's really just a cultural difference. The techniques for analyzing ML models are all statistical to begin with.
Statistics, as a field, already used general-purpose optimization algorithms before modern ML techniques came about, so in that sense, ML just fits into an existing position in the statistical toolbox (like replacing a chisel with a 3D printer). In the other direction, statistical techniques like cross-validation are necessary for you to get your ML correct.
There is much more in ML than just statistics. I was basically asking why the "statistics filter" is so often on in ML. Neural networks don't seem a statistical technique, even if somebody uses them for regression. Yes, there is an overlap, but no, ML != statistics. As you mentioned, non-linear optimization is used in statistics on meta-level however nobody claims statistics is operations research or vice versa.
I agree with this comment because ML comprises some techniques that are still not understood [1].
I would like to think that statistics comes more from a pure math approach, loosely, while ML comes from an applied math approach, loosely. ML works spectacularly well on a class of problems. Why it does what it does is (in)conveniently brushed under the rug. How you treat that (in)convenience is left to you.
In what way is neural network not a statistical technique ?
very curious to hear about your point of view. Statistics is not linear regression and ANOVA, or whatever catalogue of techniques in a freshman book, not even the library of techniques available in R.
Statistics is the application of probability (or more broadly, math) to data.
That said statisticians did miss the neural net wave because of their flippant reaction to it. They said, "oh well yet another non-parametric function approximator we have worked out the asymptotics 30 years ago".
To paraphrase someone wise: asymptotically we are all dead. Not enough heed was paid to that. Among their other lacks were expertise in algorithms and optimization. Mind you, optimization has been at the core of their craft from their very genesis, its just that they did not feel it important enough to ride the cutting edge of research on optimization. Note: you cannot do maximum likelihood with solving an optimization problem. Gauss was doing it several hundreds of years ago for statistics. If I go on a bit further with my rant, they got a bit carried away with their fetish over bias and asymptotic normality. They missed the wave, sure.
But all said and done, by any accepted definition of statistics, NN is very much also statistics.
Statisticians are either Frequentists, or Bayesianists. Fundamental to both of these approaches is the involvement of probability. A technique that does not have a probabilistic interpretation is not a statistical technique. This is also largely related to the difference in goals between statistics and machine learning: statistics is primarily about inference, and machine learning is primarily about prediction. You can predict without necessarily making any meaningful probabilistic statements about data. There's not really much to infer without saying something probabilistic.
Neural nets have absolutely been associated with probalistic interpretations. Good resources would be David McCay and Radford Neal. Both their approaches are Bayesian. A far more trivial way to associate a probabistic interpretation is to claim that the neural net is the conditional expectation.
And who says frequentist and Bayesian are the only two views. Where would you shelve prequential statistics then ? Or nonparametric regression
Prediction has definitely been a part of statistics but often, as you rightly claim, as a byproduct. And yes i would characterize the focus of stats and ml exactly as you did
The point is not whether somebody has ever tried to associate neural nets with probability, sorry if my previous comment made it seem that way. The point is that neural nets are not fundamentally tied to it. You can try to tie them to probability, but you certainly don't have to, it mostly isn't, it isn't mostly taught that way, and the big open problems in the field don't involve it.
Statistics works the other way. You basically always start with some kind of probabilistic model. And then, if you even bother with prediction, you work towards prediction from the probabilistic model. With stats you don't need to interpret or add probability after the fact, it's already there.
Obviously stats and ML are enormous fields, with quite some overlap. And people tend to go after low hanging fruit; if many people who studied neural nets have formal probability backgrounds it simply makes sense that someone will write a paper on it. And I'm generalizing here (same goes with frequentist & Bayesian comment). But there absolutely is justification for saying "neural nets are not really a statistical technique".
I agree with a lot what you are saying, but rest assured its colored by personal rationalizations one has made while learning these things and not objective facts.
nns are in no way less fundamentally associated with probability than say linear regression. In both cases you can start with a probability model and derive the final form as a logical consequence, or you can start with the final form and slap a consistent probabilistic model on top.
The main thing is that any test you come up with that carves nns away from stats is going to carve a whole lot of other things that people have no trouble calling them stats. This controversy essentially stems from the need to claim a technique for ones own tribe and not concede to another. I am making no moral claim here just an observation and neither is it a novel one.
BTW I am firmly from the ml camp and not stats. I enjoy poking a little bit of fun at statisticians and try being gracious about their criticism of ml. That said i feel no need to make a groundless claim to a technique when they have no less rights to it in terms of objective claims. Rights steeped in culture, fiat and history are a different matter.
Yes we still need statistics. There is a huge overlap between machine learning methods and applied statistics, so much so that often there is not a clear distinction between the two.
> between machine learning methods and applied statistics (...) often there is not a clear distinction between the two.
I would say applied statistics draws a line just prior to implementation concerns (say, real-world resource usage measured in time, space and energy) whereas these would be fully within scope and of interest in machine learning.
As an example, applied statistics could provide a useful approach to a vision/image recognition problem, and this approach might be provably unrealizable in practice using real-world execution units (e.g. CUDA cores). Nonetheless, it might still be a very worthwhile theoretical result in applied statistics, although of no immediate interest within ML except to hint at potential new area of research.
It may not be true for all branches of machine learning (fuzzy logic, for example?), but the vast majority of modern ML techniques are equivalent to or can be viewed as types of statistical machine learning.
Statistics means aggregate stuff and uses simplified characteristics out of semi-structured data. ML + Big Data allows you to ask precise questions like Where? How? Which ones?
As user "highd" suggested, I think you are confusing two words. I refer to Wiktionary for definitions:
Statistics: A mathematical science concerned with data collection, presentation, analysis, and interpretation.
Statistic: A quantity calculated from the data in a sample, which characterises an important aspect in the sample (such as mean or standard deviation).
If "statistics" is the term for taking a mathematical approach to understanding data, then "machine learning" is basically an applied subset of that. But you seem to be specifically using the "a statistic" definition to describe what you think the "study of statistics" is entirely concerned with.
Why L2 regularization? Same reason. A closed form solution exists from linear algebra.
But at the end of the day, you are most interested in the expectation value of the coefficient and minimizing the squared error gives you E[coeffs] which is the mean of the coefficients.