# Why is best subset selection not favored in comparison to lasso?

I'm reading about best subset selection in the Elements of statistical learning book. If I have 3 predictors $x_1,x_2,x_3$, I create $2^3=8$ subsets:

1. Subset with no predictors
2. subset with predictor $x_1$
3. subset with predictor $x_2$
4. subset with predictor $x_3$
5. subset with predictors $x_1,x_2$
6. subset with predictors $x_1,x_3$
7. subset with predictors $x_2,x_3$
8. subset with predictors $x_1,x_2,x_3$

Then I test all these models on the test data to choose the best one.

Now my question is why is best subset selection not favored in comparison to e.g. lasso?

If I compare the thresholding functions of best subset and lasso, I see that the best subset sets some of the coefficients to zero, like lasso. But, the other coefficient (non-zero ones) will still have the ols values, they will be unbiasd. Whereas, in lasso some of the coefficients will be zero and the others (non-zero ones) will have some bias. The figure below shows it better:

From the picture the part of the red line in the best subset case is laying onto the gray one. The other part is laying in the x-axis where some of the coefficients are zero. The gray line defines the unbiased solutions. In lasso, some bias is introduced by $\lambda$. From this figure I see that best subset is better than lasso! What are the disadvantages of using best subset?

• .. and what do the curves look like when the randomness in the data causes you to select one of the many wrong subsets and the associated coefficient estimates are far from zero relative to their standard errors? Jun 9, 2018 at 13:57
• @jbowman I don't understand it very clearly, why would the randomness in the data cause me to select the wrong one? If I would use cross validation to select the best subset, I would then have smaller chances to select the wrong subset. Jun 9, 2018 at 14:11
• You seem to be equating "less bias" with "better". What brings you to place such a high value on unbiasedness? Jun 19, 2018 at 16:47

In subset selection, the nonzero parameters will only be unbiased if you have chosen a superset of the correct model, i.e., if you have removed only predictors whose true coefficient values are zero. If your selection procedure led you to exclude a predictor with a true nonzero coefficient, all coefficient estimates will be biased. This defeats your argument if you will agree that selection is typically not perfect.

Thus, to make "sure" of an unbiased model estimate, you should err on the side of including more, or even all potentially relevant predictors. That is, you should not select at all.

Why is this a bad idea? Because of the bias-variance tradeoff. Yes, your large model will be unbiased, but it will have a large variance, and the variance will dominate the prediction (or other) error.

Therefore, it is better to accept that parameter estimates will be biased but have lower variance (regularization), rather than hope that our subset selection has only removed true zero parameters so we have an unbiased model with larger variance.

Since you write that you assess both approaches using cross-validation, this mitigates some of the concerns above. One remaining issue for Best Subset remains: it constrains some parameters to be exactly zero and lets the others float freely. So there is a discontinuity in the estimate, which isn't there if we tweak the lasso $\lambda$ beyond a point $\lambda_0$ where a predictor $p$ is included or excluded. Suppose that cross-validation outputs an "optimal" $\lambda$ that is close to $\lambda_0$, so we are essentially unsure whether p should be included or not. In this case, I would argue that it makes more sense to constrain the parameter estimate $\hat{\beta}_p$ via the lasso to a small (absolute) value, rather than either completely exclude it, $\hat{\beta}_p=0$, or let it float freely, $\hat{\beta}_p=\hat{\beta}_p^{\text{OLS}}$, as Best Subset does.

This may be helpful: Why does shrinkage work?

• Hmm. I don't think this answers why best subset is worse than lasso (which is the main question here). Jun 12, 2018 at 8:04
• @amoeba: would you like to elaborate? Jun 12, 2018 at 20:39
• Well, I understood the question as asking why lasso is preferred to best subset. Imagine we put both in a cross-validation loop, and then either tune the lasso parameter or find the best subset. The lasso is usually recommended. I understood the question as asking Why? (see e.g. the title of the Q) and I am not sure your answer actually answers that. Or did I misunderstand your answer? Jun 12, 2018 at 21:00
• One remaining issue for Best Subset is that it constrains some parameters to be exactly zero and lets the others float freely, so there is a discontinuity in the estimate, which isn't there if we tweak the lasso $\lambda$ beyond a point $\lambda_0$ where a predictor $p$ is included or excluded. I'd argue that if we are essentially unsure whether $p$ should be included or not, because $\lambda\approx\lambda_0$, then it makes more sense to constrain the parameter estimate $\hat{\beta}_p$ via the lasso, rather than let it float freely. Jun 15, 2018 at 7:23
• Agree that this answer doesn't really answer the question - I've added my take on this below... Jun 19, 2018 at 16:41

In principle, if the best subset can be found, it is indeed better than the LASSO, in terms of (1) selecting the variables that actually contribute to the fit, (2) not selecting the variables that do not contribute to the fit, (3) prediction accuracy and (4) producing essentially unbiased estimates for the selected variables. One recent paper that argued for the superior quality of best subset over LASSO is that by Bertsimas et al (2016) "Best subset selection via a modern optimization lens". Another older one giving a concrete example (on the deconvolution of spike trains) where best subset was better than LASSO or ridge is that by de Rooi & Eilers (2011).

The reason that the LASSO is still preferred in practice is mostly due to it being computationally much easier to calculate. Best subset selection, i.e. using an $L_0$ pseudonorm penalty, is essentially a combinatorial problem, and is NP hard, whereas the LASSO solution is easy to calculate over a regularization path using pathwise coordinate descent. In addition, the LASSO ($L_1$ norm penalized regression) is the tightest convex relaxation of $L_0$ pseudonorm penalized regression / best subset selection (bridge regression, i.e. $L_q$ norm penalized regression with q close to 0 would in principle be closer to best subset selection than LASSO, but this is no longer a convex optimization problem, and so is quite tricky to fit).

To reduce the bias of the LASSO one can use derived multistep approaches, such as the adaptive LASSO (where coefficients are differentially penalized based on a prior estimate from a least squares or ridge regression fit) or relaxed LASSO (a simple solution being to do a least squares fit of the variables selected by the LASSO). In comparison to best subset, LASSO tends to select slightly too many variables though. Best subset selection is better, but harder to fit.

That being said, there are also efficient computational methods now to do best subset selection / $L_0$ penalized regression, e.g. using the adaptive ridge approach described in the paper "An Adaptive Ridge Procedure for L0 Regularization" by Frommlet & Nuel (2016). Note that also under best subset selection you'll still have to use either cross validation or some information criterion (adjusted R2, AIC, BIC, mBIC...) to determine what number of predictors gives you the best prediction performance / explanatory power for the number of variables in your model, which is essential to avoid overfitting. The paper "Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso" by Hastie et al (2017) provides an extensive comparison of best subset, LASSO and some LASSO variants like the relaxed LASSO, and they claim that the relaxed LASSO was the one that produced the highest model prediction accuracy under the widest range of circumstances, i.e. they came to a different conclusion than Bertsimas. But the conclusion about which is best depends a lot on what you consider best (e.g. highest prediction accuracy, or best at picking out relevant variables and not including irrelevant ones; ridge regression e.g. typically selects way too many variables but the prediction accuracy for cases with highly collinear variables can nevertheless be really good).

For a very small problem with 3 variables like you describe it is plain clear best subset selection is the preferred option though.

• What does "better" mean in the phrase "it is better than lasso"? Jun 19, 2018 at 16:48
• Why is best subset the same as using L0 penalty? Best subset selects the best subset (with the lowest validation error) among subsets of any size; at least that's what OP suggested in their question. L0 penalty requires the subset to be of size $k$ (that is defined by the regularization parameter $\lambda$); one can search for best $k$ using a validation set, and then it's the best subset of size of $k$ across all possible $k$... okay, now I see that it's the same :-) Jun 19, 2018 at 18:43
• Edited my answer a bit to give some more detail... Jun 19, 2018 at 21:58
• I don't think any of the answers are addressing the problem of stability. Like stepwise and all possible subsets regression, lasso is notoriously unstable. In other words if you were to bootstrap the entire process you'll find too much arbitrariness in the list of features selected. Jun 29, 2018 at 11:16
• Yes the variables selected by LASSO can be unstable, and this is even more so the case for best subset regression - elastic net regression is a little bit better in this respect - that tends to include far too many variables then, but selected in a more stable way, and can give better prediction accuracy under high collinearity. But a lot depends on what is the most important criterion for your application - prediction accuracy, the false positive rate of including irrelevant variables or the false negative rate of not including highly relevant variables... Jun 29, 2018 at 15:14