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Metric

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

A quantitative measure used to monitor or assess model performance rather than directly define the training objective.

A metric is a quantitative measure used to monitor or assess model performance. Unlike a loss function, it does not optimize gradient descent directly; it helps determine whether training and model choices are improving. section 5.4.1

Use evaluation metrics to support practical model-development decisions:

  • Monitor training progress and decide when further iterations stop providing enough benefit. section 5.4.1
  • Compare feature selections and hyperparameter settings against a defined performance target. section 5.4.1 section 6.3
  • Support systematic tuning by evaluating candidate settings within a cross-validation scheme that tests generalization. section 6.3

Tuning a boosting model

For an XGBoost model, first define one or more evaluation metrics, then specify the hyperparameter search space, choose cross-validation, and select a search strategy. The metrics compare candidate configurations and can also support early stopping during training. section 5.4.1 section 6.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Distinguishes metrics from optimization losses and outlines their operational uses, but does not define particular metric formulas.
Examples
Uses XGBoost training, feature selection, hyperparameter search spaces, cross-validation schemes, and early stopping as practical contexts.
What it assumes
Assumes familiarity with model training and hyperparameters while clarifying how evaluation differs from optimization objectives.
What you can do after
Evaluate and tune models by comparing configurations, monitoring training, testing generalization, and stopping unproductive iterations.
How it connects
Connects evaluation metrics with loss functions, gradient descent, feature selection, hyperparameter optimization, early stopping, and cross-validation.
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