Sometimes. In machine learning, regression, and forecasting, simple models can predict as well as—or better than—more elaborate methods, especially when training data are limited. But simplicity is not a guarantee of accuracy: if the underlying process is complex, a preference for simple models can steer learning toward the wrong model family. The practical answer is to choose the least complex model that meets the task’s validated performance and operational needs.
What does “simple” mean for a model?
There is no single measure of model simplicity. It might mean fewer parameters, a hypothesis class with less capacity, a shorter description of the model, or practical qualities such as being easier to interpret and maintain. Those measures can point in different directions. In particular, raw parameter count can be misleading for overparameterized models.
In well-conditioned, low-dimensional linear regression, parameter count can serve as a meaningful complexity measure. In other settings, complexity may also depend on the data or design matrix and the signal-to-noise ratio. A 2023 paper by Raaz Dwivedi, Chandan Singh, Bin Yu, and Martin Wainwright in the Journal of Machine Learning Research examines a data-dependent minimum-description-length measure for overparameterized models.
Why can a simple model predict surprisingly well?
A more elaborate model can fit patterns in its training data that do not hold for new observations. This is one reason training-set fit alone is not a fair comparison: the important question for prediction is how a method performs on data it did not use to fit or select the model.
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In a 2017 study, Jan M. Lichtenberg and Özgür Şimşek compared simple regression methods with state-of-the-art methods on 60 real-world datasets. The simple methods, which included equal-weights regression, routinely outperformed the state-of-the-art methods, particularly with small training sets. There was no one simple method that worked well on every dataset, but nearly every dataset had at least one simple method that predicted well. This is evidence that simple baselines deserve a serious comparison—not that one simple model will work everywhere.
Forecasting comparisons point in a similar direction, with an important qualification about what the numbers represent. A 2016 review in the Journal of Business Research reported that complexity beyond what it called “sophisticatedly simple” improved accuracy in 16 of 97 comparisons across 32 papers. That is the review’s tally of published comparisons, not the probability that added complexity will help on a new forecasting task.
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When can preferring simplicity go wrong?
Simplicity preferences can help when the real process is simple, but they can also favor the wrong model family when the process is complex and the available training set is relatively small. In their 2022 theoretical analysis, Falco J. Bargagli Stoffi, Gustavo Cevolani, and Giorgio Gnecco show that regularization can reduce the sample size needed to select the correct family when the generating process is simple. Under a complex-world scenario with relatively few training examples, however, regularization can instead favor an incorrect simple family. Their analysis also shows that, given sufficiently many examples, regularized and unregularized procedures can select the correct family with a desired probability guarantee.
This is a theoretical result under the paper’s assumptions, not a survey of deployed systems or a numerical rule for how much data a particular project needs. It makes the key trade-off clear: a preference for simplicity can improve learning guarantees relative to a model family, but those guarantees do not prove that the family matches the real process.
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What does the evidence say—and not say?
| Evidence | Finding | How to interpret it |
|---|---|---|
| Lichtenberg and Şimşek, “Simple Regression Models” (2017) | Compared simple regression methods with state-of-the-art methods on 60 real-world datasets. | Simple methods were often competitive or better, especially with small training sets; results apply to the tested regression methods and datasets. |
| “Simple versus complex forecasting: The evidence” (Journal of Business Research, 2016) | Reported accuracy improvements from added complexity in 16 of 97 comparisons across 32 papers. | A count in the review, not a general success rate or forecast for a new dataset. |
| Bargagli Stoffi, Cevolani, and Gnecco, “Simple Models in Complex Worlds” (2022) | Analyzed how regularization, sample size, and whether the generating process is simple or complex affect model-family selection. | A theoretical result under stated assumptions; it does not supply a universal practical sample-size threshold. |
These findings concern supervised machine learning, regression, and forecasting, alongside statistical learning theory. They do not establish a general verdict for every scientific explanation, causal model, or application area. Nor do they establish one agreed numerical definition of a “simple model.”
How should you compare a simple model with a more complex one?
Compare candidate models against the use case, not against an abstract preference for elegance or sophistication. A useful evaluation should address these questions:
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- How well does each predict unseen cases? Choose an evaluation procedure and metric suited to the intended prediction setting. Keep training fit separate from out-of-sample performance.
- How much training data is available? Note whether model selection is sample-limited. The effect of regularization can depend on both sample size and whether the underlying process is simple or complex.
- What do you mean by complexity? State whether the comparison concerns parameter count, model-class capacity, description length, or a practical property such as interpretability. Do not use parameter count as a universal proxy, particularly for overparameterized models.
- Who needs to understand or scrutinize the predictions? Assess whether the model is understandable at the level required by the people who will use or review it. Interpretability alone does not establish accuracy, and a complex model is not necessarily impossible to interpret.
- What will it take to run and maintain? Account for computational cost and implementation or maintenance burden when those constraints matter. Practical reasons to prefer a simpler method can remain relevant even when simplicity does not identify the correct data-generating family.
If a more complex model scores better by a small amount, do not treat the difference as meaningful without specifying the metric, evaluation protocol, and uncertainty. The studies discussed here do not establish a universal threshold at which added complexity is worth the cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.So, are simple models undervalued?
They can be, when complexity is treated as a synonym for predictive quality or when a simple baseline is skipped. The evidence instead supports testing suitable simple methods alongside more complex candidates, then keeping added complexity only when validated performance or operational needs justify it. That is a decision rule for the machine-learning, regression, and forecasting settings covered here—not proof that simple models are always best.
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