Introduction

A model is not a result until it has a score, a split, and a baseline. After this chapter the reader can choose a metric, a validation scheme, and a test that would catch a broken dataset, and calibration, explanation, and fairness wait in Responsible AI.

The score comes first. Evaluation Metrics collects the supervised and unsupervised measures, from accuracy and precision/recall through ROC, F1, and perplexity, with a reality check on the metric-learning literature. A score is only as honest as the data under it, so Datasets Reliability & Correctness is about why we shouldn't trust models: dataset ablation, shortcut cues, and behavioral testing that ask whether a dataset actually requires the reasoning you think it does.

Once the score and the data can be trusted, the model has to keep earning them. Training Strategies is the framework for a continuous training strategy, with periodic and performance-based retraining driven by data changes, window size, and what to retrain. Hyper Parameter Optimization is searching hyperparameters with Hyperopt, HyperparameterHunter, and a comparison of optimizers, without losing track of experiments.

The last two pages turn evaluation into something a pipeline runs and a field compares. Data & Model Tests validates data in ML pipelines and unit tests models, from Great Expectations-style data checks to pytest and mocks. Benchmarking collects benchmarks across datasets, algorithms, hardware, cloud providers, and platforms, including NLP and multi-task learning leaderboards and notes on scaling networks.

All of it rests on the split being real. Data Scientists — Stop Using A Random Seed (April 2022) is why a score needs a real split.