ML Model Evaluation: Metrics, Tests, Baselines

To assess a machine learning model, you compare its performance on held-out data using appropriate metrics against a baseline (like a simple heuristic or a prior model) and validate the results with statistical tests. This article explains how to choose metrics, set baselines, run tests, and avoid common pitfalls.

Formula

The calculator above computes average pace and speed from a finish time and distance. The formulas are:

• Pace (min/km) = Finish time (min) / Distance (km)
• Speed (km/h) = Distance (km) / (Finish time (min) / 60)

Where:

• t = finish time in minutes
• d = distance in kilometers

Pace is the number of minutes to cover one kilometer; speed is the number of kilometers covered in one hour. They are reciprocals when units are consistent.

Worked Example

Using the default values: t = 50 minutes, d = 10 km.

1. Pace = 50 / 10 = 5.00 min/km
2. Speed = 10 / (50 / 60) = 10 / 0.8333 = 12.00 km/h

The calculator shows 5.00 min/km and 12.00 km/h for those inputs.

Why Baselines Matter

A baseline is a simple reference model that gives you a lower bound for acceptable performance. Without a baseline, a model with 90% accuracy might seem impressive, but if 90% of your data belongs to one class, a naive classifier that always predicts that class would also get 90%. Common baselines include:

• Majority class – always predict the most frequent label.
• Random guessing – predict according to class distribution.
• Simple heuristics – e.g., predicting the mean for regression, or using a linear model.
• Previous model – the model currently in production.

Your model should beat the baseline by a meaningful margin, not just by a hair, because the baseline is usually much simpler and faster.

Choosing the Right Metrics

Accuracy is not enough for most real-world problems. The choice of metric depends on your task and the cost of errors.

• Classification: Use precision, recall, F1-score, ROC-AUC, or PR-AUC. For imbalanced classes, prefer precision and recall over accuracy.
• Regression: Use Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or R-squared. MAE is robust to outliers, while RMSE penalizes large errors more.
• Ranking: Use NDCG, Mean Reciprocal Rank, or Precision@k.
• Probabilistic: Use Log Loss or Brier score.

Always pick metrics that reflect the real-world cost of mistakes. For example, in medical screening, a false negative is often far worse than a false positive, so recall is prioritized.

Validation Methods and Statistical Tests

You cannot assess a model on the same data it was trained on. Standard practice is to split data into training, validation, and test sets. For small datasets, use k-fold cross-validation (e.g., 5-fold or 10-fold) to get a more stable estimate of performance.

Once you have performance numbers, you need to know if differences are real or due to chance. Simple tests include:

• Paired t-test – compare two models’ performance across folds or repeated runs.
• Wilcoxon signed-rank test – non-parametric alternative to the t-test.
• McNemar’s test – for comparing two classifiers on a single test set based on correct/incorrect predictions.

Always report the mean and standard deviation of your metric across folds, and state the test used to compare models.

Common Mistakes and Pitfalls

• Data leakage – using information from the test set during training (e.g., scaling before splitting). Always split first, then fit preprocessing on training data only.
• Overfitting the test set – if you evaluate many models on the same test set, you are implicitly tuning to it. Use a separate validation set for tuning and a final test set only once.
• Ignoring class imbalance – accuracy is misleading when one class dominates. Use precision/recall or AUC.
• Comparing models on different data splits – always use the same folds or seeds for a fair comparison.
• Not checking statistical significance – a 0.5% difference in accuracy may be noise, especially on small test sets.

How to Read the Results

After evaluating, ask yourself: does the model’s performance meet the business or application requirement? For example, a spam filter might need high precision (few false positives) even if recall is moderate. Also consider the confidence intervals around your metrics – a wide interval means you need more data or more folds to be sure.

Compare your model to the baseline and to simpler models. If a linear model performs nearly as well as a deep neural network, the extra complexity may not be worth it. Also consider inference time, memory usage, and interpretability in real deployment.

Frequently Asked Questions

What is a good baseline for a machine learning model?

A good baseline is the simplest model that captures the basic structure of the data. For classification, the majority class is a common starting point. For regression, the mean of the target variable. You can also use a linear model (logistic regression for classification, linear regression for regression) as a stronger baseline.

How many metrics should I report?

Report 2-4 metrics that are most relevant to your problem. For classification, precision, recall, F1, and ROC-AUC are typical. For regression, MAE and RMSE. Too many metrics can confuse stakeholders; focus on the ones that matter for the use case.

What is the difference between validation and test set?

The validation set is used to tune hyperparameters and make model selection decisions during development. The test set is used only once, at the end, to estimate how the model will perform on unseen data. Using the test set multiple times leads to overfitting the test set.

When should I use cross-validation instead of a single train-test split?

Use cross-validation when your dataset is small or when you need a more reliable estimate of performance. It uses all data for both training and validation, reducing variance. For very large datasets, a single split is often sufficient and more computationally efficient.

How do I know if my model is overfitting?

Overfitting occurs when the model performs well on training data but poorly on validation/test data. Compare training and validation metrics. A large gap (e.g., 99% training accuracy vs. 85% validation accuracy) indicates overfitting. Solutions include regularization, more data, or simpler models.

Can I use the test set multiple times?

No. The test set should be used only once to avoid biasing your model selection. If you need to evaluate multiple models, use a validation set or cross-validation. After you have chosen the final model, you can evaluate it on the test set exactly once.

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in t | Finish time | min | 50 | 0
in d | Distance | km | 10 | 0
out pace | Average pace | min/km | t / d | 2
out speed | Average speed | km/h | d / (t / 60) | 2