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Your first machine-learning model should be simple enough to look unimpressive: its job is to establish a performance floor, not to win a place in production. Compare it with a trivial predictor and a simple learned model before deciding whether a more complex model or tuning actually earns its extra cost.
What does the baseline tell me?
A baseline gives later scores a reference point. If a complex model reports 95% accuracy, that number means little until you know what a no-skill or simple model achieves on the same task and evaluation data. A baseline can also expose problems with the metric: when one class is much more common than another, guessing that class every time may produce high accuracy while failing to identify the cases that matter.
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Google’s Rules of Machine Learning puts the purpose plainly: “Your simple model provides you with baseline metrics and a baseline behavior that you can use to test more complex models.” The advice is to get a reliable starting point, then use it to measure what additional complexity changes—not to assume that simple models are always best.
For classification, scikit-learn’s version 0.16.1 documentation describes DummyClassifier as useful “as a simple baseline to compare with other (real) classifiers.” That is version-specific documentation, not a statement about the current API. The general idea is to choose a deliberately simple rule suited to the task; for regression, that might be a constant prediction rather than a classifier’s majority-class guess.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
How much did the complex model improve over the simple one?
Compare models on the same held-out data, with the same metric and evaluation procedure. A useful progression is a trivial predictor, a simple learned model, and then increasingly complex or tuned models. This separates two questions: whether learning helps at all, and whether the added complexity helps beyond a reasonable simple model.
| Comparison | What it helps establish |
|---|---|
| Trivial predictor vs. existing process | Whether a basic reference is competitive with the current operational approach. |
| Simple learned model vs. trivial predictor | Whether the available features and training process provide measurable predictive value. |
| Complex or tuned model vs. simple learned model | Whether added complexity or tuning produces enough improvement to justify its costs. |
Choose a metric based on the task’s error costs and class balance, not habit. Accuracy may be appropriate in some settings, but it can hide poor performance on a minority class. Depending on the objective, a metric that reflects ranking, precision, recall, or another consequence of errors may be more informative. Decide what success means before looking at model scores.
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What does one reported comparison show?
In “Your First Model Should Be Embarrassing,” Jason Lau reports a four-rung comparison—majority-class guess, logistic regression, default boosted trees, and tuned boosted trees—on six public binary-classification datasets. The figures below describe Lau’s stated experiment, not independently reproduced results or expected outcomes for other datasets.
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- Lau reports that a 200-fit tuning search improved AUC by more than half a point on one of the six datasets, with little or no gain on most of the others in that run.
- In the stated setup, default boosted-tree fits took under a second per dataset, while tuning searches took 43–152 seconds per dataset on a four-core machine.
The results vary by dataset and random split. They illustrate why a baseline and a consistent comparison matter; they do not show that simple models always beat complex ones, that tuning is never worthwhile, or that the same runtime and performance differences will recur on other machines or data.
How do I keep the comparison fair?
Establish the metric and evaluation design before comparing model families. Google’s Experiments guidance recommends determining baseline performance, making small changes, and recording results. It also warns that small evaluation sets can produce uneven estimates, so a small apparent gain may not be reliable.
- Define the prediction objective. Specify what the model is meant to predict and which errors matter most. Choose a metric that reflects that objective.
- Record the non-ML reference. If there is an existing business rule or process, measure its performance where an appropriate comparison is possible. A model can beat a trivial predictor yet still fail to improve on the process it would replace.
- Fit a task-appropriate trivial baseline. For classification, a majority-class or other simple rule may be useful; for regression, use an appropriate constant predictor. Do not apply one recipe to every problem.
- Fit a simple learned model. Logistic regression is one possible starting point for suitable classification tasks. Evaluate it using the same split and metric as the trivial baseline.
- Change one thing at a time. Add model complexity or tune parameters in controlled steps, recording the metric and the evaluation setup for each change.
- Check whether the difference is dependable and useful. Account for variability, especially with small evaluation sets, and ask whether the measured gain is large enough to matter in practice.
When has a more complex model earned its place?
A higher score alone is not the whole decision. Weigh the measured gain against the computation, maintenance, interpretability, and other constraints involved in using the model. Where decisions need to be explained or additional requirements apply, include those costs in the comparison rather than treating predictive performance as the only criterion.
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A baseline is a floor for comparison, not proof that deployment is valuable. The complex model must improve on the relevant alternatives by enough, with an evaluation result reliable enough, to justify its added cost.
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