The Bias-Variance trade-off : Explanation and DemoAditya PrasadBlockedUnblockFollowFollowingApr 5The Bias-Variance trade-off is a basic yet important concept in the field of data science and machine learning.

Often, we encounter statements like “simpler models have high bias and low variance whereas more complex or sophisticated models have low bias and high variance” or “high bias leads to under-fitting and high variance leads to over-fitting”.

But what do bias and variance actually mean and how are they related to the accuracy and performance of a model?In this article, I will explain the intuitive and mathematical meaning of bias and variance, show the mathematical relation between bias, variance and performance of a model and finally demo the effects of varying the model complexity on bias and variance through a small example.

Assumptions to start withBias and variance are statistical terms and can be used in varied contexts.

However, in this article, they will be discussed in terms of an estimator which is trying to fit/explain/estimate some unknown data distribution.

Before we delve into the bias and variance of an estimator, let us assume the following :-There is a data generator, Y = f(X) + ϵ, which is generating Data(X,Y), where ϵ is the added random gaussian noise, centered at origin with some standard deviation σ i.

e.

E[ϵ] = 0 and Var(ϵ) = σ² .

Note that data can be sampled repetitively from the generator yielding different sample sets say Xᵢ , Yᵢ on iᵗʰ iteration.

We are trying to estimate(fit a curve) to the sample set we have available from the generator, using an estimator.

An estimator usually is a class of models like Ridge regressor, Decision Tree or Support Vector Regressor etc.

A class of models can be represented as g(X/θ) where θ are the parameters.

For different values of θ, we get different models within that particular class of models and we try vary θ to find the best fitting model for our sample set.

Meaning of Bias and VarianceBias of an estimator is the the “expected” difference between its estimates and the true values in the data.

Intuitively, it is a measure of how “close”(or far) is the estimator to the actual data points which the estimator is trying to estimate.

Notice that I have used the word “expected” which implies that the difference is being thought over keeping in mind that we will be doing this model training experiment (ideally)infinite number of times.

Each of those models will be trained on different sample sets Xᵢ , Yᵢ of the true data resulting in their parameters taking different values of θ in a bid to explain/fit/estimate that particular sample best.

Eventually for some test point xₒ , the bias of this estimator g(X) can be mathematically written as :-Bias[g(xₒ)] = E[g(xₒ)] − f(xₒ)which is literally the difference between the expected value of an estimator at that point and the true value at that same point.

Naturally, an estimator will have high bias at a test point(and hence overall too, in the limit) if it does NOT wiggle or change too much when a different sample set of the data is thrown at it.

This will usually be the case when an estimator does not have enough “capacity” to adequately fit the inherent data generating function.

Therefore, simpler models have a higher bias compared to more sophisticated models.

Hold these thoughts and we will come back to them again later in the article.

For now, here is a figure to help solidify them a bit more.

Linear Regression fits for two different samples of size 8.

Notice how curve has not changed too much although the sample sets are totally disjointVariance of an estimator is the “expected” value of the squared difference between the estimate of a model and the “expected” value of the estimate(over all the models in the estimator).

Too convoluted to understand in a go?.Lets break that sentence down.

Suppose we are training ∞ models using different sample sets of the data.

Then at a test point xₒ, the expected value of the estimate over all those models is the E[g(xₒ)].

Also, for any individual model out of all the models, the estimate of that model at that point is g(xₒ).

The difference between these two can be written as g(xₒ) − E[g(xₒ)].

Variance is the expected value of the square of this distance over all the models.

Hence, variance of the estimator at at test point xₒ can be mathematically written as :-Var[g(xₒ)] = E[(g(xₒ) − E[g(xₒ)])²]Going by this equation, we can say that an estimator has high variance when the estimator “varies” or changes its estimate a lot at any data point, when it is trained over different sample sets of the data.

Another way to put this is that the estimator is flexible/sophisticated enough, or has a high “capacity” to perfectly fit/explain/estimate the training sample set given to it due to which its value at other points fluctuates immensely.

Support Vector Regressor fits for the same sample sets.

Notice how the curve changed drastically in this case.

SVR is a high capacity estimator compared to Linear Regression hence higher varianceNotice that this interpretation of the meaning of high variance is exactly opposite to that of an estimator having high bias.

This implies that bias and variance of an estimator are complementary to each other i.

e.

an estimator with high bias will vary less(have low variance) and an estimator with high variance will have less bias(as it can vary more to fit/explain/estimate the data points).

The Bias-Variance DecompositionIn this section, we see how the bias and variance of an estimator are mathematically related to each other and also to the performance of the estimator.

We will start with defining estimator’s error at a test point as the “expected” squared difference between the true value and estimator’s estimate.

By now, it should be fairly clear that whenever we are talking about an expected value, we are referring to the expectation over all the possible models, trained individually over all the possible data samples from the data generator.

For any unseen test point xₒ, we have :-Err(xₒ) = E[(Y − g(xₒ))² | X = xₒ]For notational simplicity, I am referring to f(xₒ) and g(xₒ) as f and g respectively and skipping the conditional on X :-Err(xₒ) = E[(Y − g(xₒ))²]= E[(f + ϵ − g)²]= E[ϵ²] + E[(f − g)²] + 2.

E[(f − g)ϵ]= E[(ϵ − 0)²] + E[(f − E[g] + E[g] − g)²] + 2.

E[fϵ] − 2.

E[gϵ]= E[(ϵ − E[ϵ])²] + E[(f − E[g] + E[g] − g)²] + 0 − 0= Var(ϵ) + E[(g − E[g])²] + E[(E[g] − f)²] + 2.

E[(g − E[g])(E[g] − f)]= Var(ϵ) + Var(g) + Bias(g)² + 2.

{E[g]² − E[gf] − E[g]² + E[gf]}= σ² + Var(g) + Bias(g)²So, the error(and hence the accuracy) of the estimator at an unseen data sample xₒ can be decomposed into variance of the noise in the data, bias and the variance of the estimator.

This implies that both bias and variance are the sources of error of an estimator.

Also, in the previous section we have seen that bias and variance of an estimator are complementary to each other i.

e.

the increasing one of them would mean a decrease in the other and vice versa.

Now pause for a bit and try thinking a bit about what these two facts coupled together could mean for an estimator….

The Bias-Variance Trade-offFrom the complementary nature of bias and variance and estimator’s error decomposing into bias and variance, it is clear that there is a trade-off between bias and variance when it comes to the performance of the estimator.

An estimator will have a high error if it has very high bias and low variance i.

e.

when it is not able to adapt at all to the data points in a sample set.

On the other extreme, an estimator will also have a high error if it has very high variance and low bias i.

e.

when it adapts too well to all the data points in a sample set(a sample set is an incomplete representation of true data) and hence fails to generalize other unseen samples and eventually fails to generalize the true dataset.

An estimator that strikes a balance between the bias and variance is able to minimize the error better than those that live on extreme ends.

Although it is beyond the scope of this article, this can be proved using basic differential calculus.

Courtesy : The Elements of Statistical Learning by Jerome H.

Friedman, Robert Tibshirani, and Trevor Hastie.

Blue curves show the training errors on 100 samples of size 50.

Red curves are the corresponding test set errorsThis figure has been taken from ESLR and it explains the trade-off very well.

In this example, 100 sample sets of size 50 have been used to train 100 models of the same class, each of whose complexity/capacity is being increased from left to right.

Each individual light blue curve belongs to a model and demonstrates how the model’s training set error changes as the complexity of the model is increased.

Every point on the light red curve in turn is the model’s error on a common test set, tracing the curve as the complexity of the model is varied.

Finally, the darker curves are the respective average(tending to expected value in the limit) training and test set errors.

We can see that the models that strike a balance between bias and variance are able to generalize the best and perform substantially better than those with high bias or high variance.

DemoI have put up a small demo showing everything I have talked about in this article.

If all of this makes sense and you would like to try it out on your own, do check it out below!.I have compared the bias-variance tradeoff between Ridge regressor and K-Nearest Neighbor Regressor with K = 1.

Keep in mind that KNN Regressor, K = 1 fits the training set perfectly so it “varies” a lot when the training set is changed while Ridge regressor does not.

Demo comparing bias-variance between KNN and Ridge RegressorsI hope this article explained the concept well and was fun to read!.If you have any follow up questions, please post a comment and I will try to answer them.

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