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Average precision

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

A classification metric that summarizes the precision-recall curve, typically by averaging precision over recall increments.

Average precision summarizes the precision-recall curve by averaging precision across recall increments. It is useful when a binary target is imbalanced and ranking positive cases matters more than raw accuracy. section 4.2.1

Skewed price target

Classifying listings as above or below the mean price produces 34,016 negative cases and 14,879 positive cases. For this imbalanced target, average precision is preferred alongside ROC-AUC rather than relying on accuracy alone. section 4.2.1

target_mean = (data["price"] > data["price"].mean()).astype(int)
target_median = (data["price"] > data["price"].median()).astype(int)
target_multiclass = pd.qcut(data["price"], q=5, labels=False)
target_regression = data["price"]

Inspect the target transformations that create binary, multiclass, and regression tasks from the price column. section 4.2.1

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

How deep it goes
Defines the metric’s practical role but stops short of deriving or implementing the precision-recall calculation.
Examples
Uses an above-mean price target with 34,016 negative and 14,879 positive cases as the imbalanced classification example.
What you can do after
Evaluate imbalanced binary classifiers with a ranking-sensitive metric instead of relying only on accuracy.
How it connects
Connects average precision with precision-recall curves, ROC-AUC, binary classification, and imbalanced classes.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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