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A baseline model is a simple, well-defined reference used to evaluate whether a new model provides meaningful improvement. In practice, you feed the same input to both a BaselineModel and your TrainModel and compare their predictions and metrics. The purpose of a baseline is to establish a minimum acceptable performance and to justify added model complexity when the trained model exceeds that baseline.
The image illustrates a flowchart showing a baseline concept overview, where input data is processed through a baseline model and a trained model to generate and compare predictions.

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Common baseline types

Use increasingly stronger baselines as you progress: start simple and advance toward more competitive references. Below is a concise description of common baseline classes.
Choose baselines appropriate to your problem and dataset. Start with the simplest (naive) and progress to heuristics, simple models, and SOTA baselines until you establish a realistic target for development and deployment.

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Why baselines matter (example)

Suppose a baseline reaches 70% accuracy and your trained model reaches 85% accuracy — a 15 percentage-point improvement. That gap quantifies the model’s added value and helps decide whether extra complexity is justified.
The image illustrates a comparison between a baseline model with 70% accuracy and a trained model with 85% accuracy, highlighting a 15% improvement in accuracy indicating meaningful learning.

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Naive baselines by problem type

Naive baselines are fast to compute and useful as the first checkpoint. They intentionally ignore patterns in the features and act as a simple reference. The plot below illustrates a naive regression baseline predicting the global mean as a constant (dashed line) while the actual target values vary over time. This highlights how naive approaches provide a minimal benchmark that more sophisticated models should surpass.
The image is a line graph comparing "Naive Regression Baseline vs Actual" values over different time steps, with actual values depicted as a solid line and the naive baseline as a dashed line. The graph includes labeled axes for target value and time step.

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Heuristic baselines

Heuristic baselines apply simple, interpretable rules derived from domain knowledge (for example, y = 2x). They often outperform naive baselines because they encode known relationships and provide a stronger, explainable benchmark. Use these to confirm whether model complexity is adding value beyond simple rules.

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Model-based baselines

Model-based baselines use basic machine learning algorithms to create a data-adaptive reference. These typically outperform naive and heuristic baselines and are essential for structured data problems. Common simple-model baselines:
  • Linear regression / logistic regression for tabular data
  • Decision stumps (single-level decision trees)
  • k-Nearest Neighbors (kNN), often with k=1 or small k
These model families are lightweight, fast to train, and help set realistic expectations before moving to more complex architectures.
The image describes three simple machine learning models: linear/logistic regression for structured data, decision stumps (single-level decision trees), and nearest neighbor approaches (kNN with k=1).
A model-based baseline such as linear regression adapts to the data and typically produces a better fit than a heuristic rule. The example below shows a linear baseline described by y = 2.00x + 0.43 (dashed line) compared to actual values — a clear, data-driven reference.
The image shows a graph comparing model-based baseline values (a dashed blue line) and actual values (an orange line with dots) plotted against a feature (x-axis) and target value (y-axis). The baseline model follows the equation (y = 2.00x + 0.43).

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State-of-the-art (SOTA) baselines

SOTA baselines benchmark your approach against the best published or industry-standard models. These are useful to determine whether your model is competitive:
  • If SOTA = 90% and your model = 85% → further improvements needed.
  • If your model approaches SOTA, you may focus on deployment, inference optimization, or cost/performance trade-offs.
The image compares a baseline model with 90% accuracy to a trained model with 85% accuracy, highlighting the concept of state-of-the-art (SOTA) baselines.

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Practical checklist for baselines

  • Establish at least one simple baseline (naive or random) before model tuning.
  • Iterate: naive → heuristic → simple model → SOTA as needed.
  • Use the baseline gap (performance delta) to quantify business value and justify complexity.
  • Report baselines alongside model metrics in documentation and model cards for transparency.
  • Re-evaluate baselines when data, labels, or business objectives change.
Resources and further reading: By defining clear baselines early, you save development time, avoid unnecessary complexity, and focus on changes that deliver measurable value.

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