This chart paints several trends around the idea of model variance. First though, let's arm ourselves by actually defining model variance. For a particular value of $X=x$: \n",
"\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imp.reload(bv)\n",
"res = bv.getVarianceTrend(20, b)\n",
"bv.plotVarianceTrend(res, (15, 9))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This plot should confirm several of our expectations. First, when the complexity of our models increase (measured by the degree of the polynomial), model variance increases. Also, variance decreases as we increase the sample size. Beyond that, note the shape of the curves in the top plot above (for any model degree). We can see that doubling the data (each tick on the x-axis represents a doubling) cuts the variance in half. For instance, with $d = 4$ (our one unbiased model family), going from $2^{10}$ to $2^{11}$ records drops the variance around 50%. The bottom chart shows both the sample size and model on the log2 scale. We can see that relationship on the log-log scale is nearly perfectly linear. Every unit increase in log2 scale decreases the log2 of the variance by a constant amount, such that: $log_2(\\hat{\\sigma_m}^2) = -log_2(N) + c$. With a little algebra we get to $\\hat{\\sigma_m}^2 \\propto N^{-1}$. Here we have shown empirically what is well known analytically - that model variance reduces at the rate of $O(N^{-1})$. For reasons that we'll discuss later, investing in more and more data to reduce the variance doesn't always pay off (in terms of reducing error to balance out the additional data cost).\n",
"
\n",
"\n",
"## Model Error, Bias and Variance\n",
"\n",
"So at this point we should have a solid foundation as to what exactly we mean by a model's bias and variance. And most importantly, we should now be able to understand the drivers of model bias and variance. Understanding such drivers gives the modeler an element of control over the two phenomena. But why do we need to control such things? After all, our modeling goal is generally not to hit some target level of bias or variance. Rather, we want to reduce the prediction error of our model. It turns out, bias and variance are intimately connected to model error, such that the total model error is often explicitly determined by the amount of bias or variance. Thus, controlling bias and variance in essence helps us control the error.\n",
"
\n",
"\n",
"### Bias-Variance Decomposition for Least-Squares Error\n",
"\n",
"\n",
"The most famous and convenient representation of this relationship is done using least-squares error. For problems in which MSE (least squares) is our error function, we can express the MSE explicitly in terms of model bias, model variance and the underlying noise in our target data (which is often called the 'irreducible error', for reasons discussed later on). In this section we'll derive this famous decomposition. \n",
"\n",
"Important Concepts Defined \n",
"\n",
"First, let's assume our data comes in pairs $(X, Y)$, and our general estimation goal is to learn a function $f(X)$ such that $f(x) = E[Y|X=x]$. Because our data is finite, we never know the true $E[Y|X=x]$, but we can estimate $f_j(x) = \\hat{Y}_x = E_j[Y|X=x]$ over some fixed, finite sample $j$. Our MSE at a give value of $X=x$, with known $Y=y$ is defined as:\n",
"\n",
"
$MSE(x, y) = (y - \\hat{Y}_x)^2$ \n",
"(note, for notational simplicity we ignore the fact that MSE is often computed and averaged across all instances of the data. The derivation still holds for a single instance). \n",
"\n",
"Before we continue, we need to introduce and explain some important concepts and quantities. \n",
"\n",
"\n",
" Family of functions $\\mathbb{F}$ : This is a finite set of functions that share some structural properties. For instance, all decision trees on a set of features with depth=10, or all linear functions of the form $f(X)=\\beta^T \\: X$. Generally any classification algorithm can be placed into some particular family $\\mathbb{F}$. \n",
" \n",
"\n",
"\n",
" Expected vs. Empirical risk: The expected risk is a theoretical property we could know if we knew the true distribution $P(X,Y)$. For MSE the expected risk of a given function $f$ is $E[MSE(f)]= \\int ((y-f)^2 \\: dP(X,Y)$. Since we generally don't know $P(X,Y)$, we have to approximate the expected risk with the empirical risk, which is defined as: $E_j[MSE(f)] =n^{-1}\\sum ((y_i - f_i)^2$, where the sum is taken over some finite data set $j$. \n",
" \n",
"\n",
"\n",
" The true risk minimizer $f^*$ : This is the function $f^*$ that best approximates the true $E[Y|X=x]$ and is the one that minimizes the expected risk across all families of functions. It can be defined as: $f^*= \\underset{f} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. In other words, this is the absolute truth that we wish we knew, but generally have to approximate using a finite data sample and some estimation procedure. \n",
" \n",
"\n",
"\n",
" The expected risk minimizer from family $\\mathbb{F}$, $\\:\\:f^{\\mathbb{F}}$ :This is similar to the true risk minimizer, only that we restrict the exploration of candidate functions to only those included in $\\mathbb{F}$. The exact definition is: $f^{\\mathbb{F}}= \\underset{f \\in \\mathbb{F}} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. One way to think about this is, using the above example, imagine we had infinite data but could only fit some non-linear data scatter with a linear model - the result wold be the expected risk minimizer from the family of linear functions on $X$. \n",
" \n",
"\n",
"\n",
" The emprical risk minimizer from family $\\mathbb{F}$, $\\:\\:f_j^{\\mathbb{F}}$ This is similar to the above definition, but using the empirical risk definition as opposed to the expected risk definition. When we fit a model on finite data, this is generally the function we end up with. \n",
" \n",
"\n",
"\n",
"Derivation of MSE Bias-Variance Decomposition \n",
"\n",
"So now we have 3 functions defined, $f^*$, $f^{\\mathbb{F}}$ and $f_j^{\\mathbb{F}}$. We'll first rewrite MSE using this new notation:$MSE = (y - f_j^{\\mathbb{F}})^2$. Also, as our goal is to generalize to new data, we care more about the expected MSE, or $E[(Y - f_j^{\\mathbb{F}})^2]$. What we'll now do is use algebra and some statistical rules to decompose this expected MSE into more familliar terms: \n",
"\n",
"$E[(Y - f_j^{\\mathbb{F}})^2] = E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2]$ \n",
"\n",
"Using the rule that $E[X^2] = Var[X] + E[X]^2$, and a little algebra, we can expand this to: \n",
"\n",
"$E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2] = Var[Y]+E[Y]^2 + Var[f_j^{\\mathbb{F}}]+E[f_j^{\\mathbb{F}}]^2- 2E[Yf_j^{\\mathbb{F}}] = Var[Y]+ Var[f_j^{\\mathbb{F}}] + (E[Y] - E[f_j^{\\mathbb{F}}])^2$ \n",
"\n",
"Now, according to our definitions above $Y = f^* + \\epsilon$, giving us $E[Y] = E[f^*] = f^*$., and $E[f_j^{\\mathbb{F}}] = f^{\\mathbb{F}}$ (remember, the expectations are taken over the true distribution so these terms are deterministic). Also, since $f^*$ is deterministic and independent of $\\epsilon$, $Var[Y] = Var[f^*] + Var[\\epsilon] = \\sigma^2$. Putting this all together, we get the punchline of all of the above: \n",
"\n",
"$E[(Y - f_j^{\\mathbb{F}})^2] = \\sigma^2 + E[(f_j^{\\mathbb{F}} - f^{\\mathbb{F}})^2] + (f^*-f^{\\mathbb{F}})^2 = Irreducible Error + Variance + Bias^2$ \n",
"\n",
"Getting to this point was quite a feat, but now we have a great framework for understanding how and why our models may be wrong. Note that although we were able to derive an exact analytic bias-variance decomposition for MSE, other loss-functions (LogLoss, MAE, etc.) don't provide us with the same luxury. Nonetheless, the general framework is the same, and what we learn qualitatively from the MSE case can be applied to the Non-MSE case.\n",
"\n",
"\n",
"## Improving Models Using the Bias-Variance Framework\n",
"\n",
"Understanding the bias-variance decomposition isn't just a theoretical exercise. Quite the opposite, it is the most fundamental principal that governs model generalizability and its lessons are directly applicable to empirical problems. Here are some key takeaways that we can highlight based on the above analysis that can be directly applied to our work. \n",
"\n",
"More data should not hurt, and is likely better: \n",
"This is probably an obvious statement based on everything we hear about \"Big Data,\" but now we have a framework to understand why. Having more data examples reduces model estimation variance at the rate of $O(N^{-1})$, which all else being fixed (remember, more data doesn't change the bias or irreducible error given a fixed model family), reduces the total error. An important caveat is that the value of this variance reduction might not be proportional to the costs associated with increased data. This is a very problem specific trade-off that should at least always be considered (remember, data costs can be in actual economic currency or CPUs). Also, having more data doesn't guarantee measurable results. There are always diminishing returns, so the improvement will depend on where you are on the sample size vs performance curve.\n",
"\n",
"Model complexity is your friend, but it can stab you in the back if you are not careful: \n",
"The bias part of the error decomposition is purely a function of your model specification and is independent of the data (specifically, the number of examples, not features, in your data). One can often get better results by using more complex (flexible) modeling algorithms. Example ways to add model complexity are: adding new features to the dataset, using less regularization in linear models, adding non-linear kernel functions to SVMs, using Neural Networks or Decision Tree based methods over linear methods. \n",
"\n",
"The major caveat is worthy of its own paragraph here. Given a finite data set, adding more complexity will almost always increase the estimation variance of your model. As discussed above, more flexible models will confuse more noise (the $\\epsilon$ part of $Y=f^*+\\epsilon$) for signal. Thus, we have to be very careful here. Adding more data given a fixed model family might waste time, money or CPUs, but it should't make your model worse. Adding more complexity can really hurt you if not done carefully. So this begs the questions: \n",
"\n",
"How much model complexity should I use? \n",
"\n",
"
\n",
"This is ultimately an empirical question, as the truth is never known outside of carefully crafted simulations. This is why we ALWAYS use train/valiation splits or cross-validation to guide our model selection. While we have a good analytic model for knowing how increased sample size will decrease variance, we don't have the same for predicting how increased complexity will increase it. Thus, we have to carefully test our models on out-of-sample data to find the empirically optimal trade-off. \n",
" \n",
" \n",
"\n",
"\n",
"How do I know if my model is suffering from too much bias or too much variance. \n",
"\n",
"The best performing model on out-of-sample data again will be the one that is 'just-right' in terms of its bias and variance. Generally, the sweet-spot will be the level of complexity in which the testing error is the lowest. Given a particular model fit, we can diagnose whether it suffers from high/low bias/variance buy comparing both the training and the testing error. If the training error and testing errors are both high, then bias is the likely culprit. If training error is low but testing error is high, then variance is likely the culprit. The following chart (taken from the book Elements of Statistical Learning 2) shows a hypothetical example. In a realistic case, the x-axis will be defined by whatever parameters define complexity for your choice of algorithms (i.e., the regularization weight, depth of trees, number of trees in a forest, etc.).\n",
"\n",
"\n",
" \n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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Jkvwt9UD6iIyjLI/sJ7l6+TgFdRJE6lLMVbJUZlAeUilVdVXjUhtBssBNg01jWPO4lqu2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j54RgMwETMIFCIPD222+jQYMGOPpo5TLZopHArifjaGyd22QCJlB0BFJW4afhP4YertWIWk17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\nBEiABEiABEiABEiguhD4P8n+UclKZHP8AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='references/bv_complexity_es2.png') "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"\n",
"In most observed phenomena, you can never get perfect predictions. Also, you shouldn't really compare models across different problems \n",
"\n",
"These are two different statements, but they're both related to that $\\sigma^2$ term, which again we call the irreducible error. The first statement should be obvious if we assume $\\sigma>0$. Almost all systems we attempt to model, especially systems that capture human dynamics, are burdened with noise. With an infinite sample size we can potentially test every possible function and arrive at both zero variance and zero bias. Even then, the quantum mechanical nature of the universe, and the fact that there is always likely some unmeasured variable (whose effect can be folded into $\\epsilon$), means that $Y$ will always have some unexplainable variance. No model and modeler will ever get beyond that point (for those interested, this error is often called the 'Bayes Error'). \n",
"\n",
"The second statement above is related to the fact that every $Y^p$ will have an associated $\\epsilon^p$. A model predicting $E[Y^1|X=x]$ may have both lower bias and variance than a model predicting $E[Y^2|X=x]$, but if $Var[\\epsilon^1]>Var[\\epsilon^2]$, the model on $Y^1$ can still have a worse error than the one for $Y^2$. Generally this isn't a problem, so long as the appropriate comparisons are made. Specifically, the qualitative assessment of a model should be relative to a baseine derived from the same problem (i.e., predicting the same $Y^p$). And in some circumstances, a model that does 5% better than random on some problem may be worth a lot more money than a model that is nearly perfect (think about predicting stocks vs. predicting sunset). \n",
"\n",
"Corrollary to the above paragraph is the following rule: don't judge a model just because its error is too close to random. Again, every model should be judged using appropriate baselines that are specific to the problem at hand. Accuracy might be far from 100%, but the model can still be good (and this relates back to how much irreducible error is in the underlying system).\n",
"
\n",
"\n",
"## Wrap Up\n",
"\n",
"At this point you should (hopefully) understand the following at both an intuitive level and foundational theoretical level:\n",
"\n",
"
\n",
" Model Bias \n",
" Model Variance \n",
" The relationship between bias, variance and prediction error \n",
" How to simulate and represent graphically model bias and variance \n",
" How to use the bias-variance tradeoff to guide your modeling decisions \n",
" \n",
"\n",
"references \n",
"\n",
"The presentation here was intentionally light on theory and mainly focused on the bias-variance decomposition of MSE. For further exploration of the topic, feel free to consult the following: \n",
" \n",
"\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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