Averaging Strategies

Last updated 2020-07-29 Read time 3 min

Summary

Understand the fundamentals of this metric, what it evaluates, and how to interpret the results.
Compute and visualise the metric with Python 3.13 code examples, covering key steps and practical checkpoints.
Combine charts and complementary metrics for effective model comparison and threshold tuning.

Precision & Recall — understanding this concept first will make learning smoother
F1 Score — understanding this concept first will make learning smoother

1. Main averaging options #

average	How it is computed	When to use it
`micro`	Sum TP/FP/FN over all samples, then compute the metric	Emphasises overall correctness regardless of class distribution
`macro`	Compute the metric per class, then take the unweighted mean	Gives every class the same weight; highlights minority classes
`weighted`	Compute the metric per class, then take a support-weighted mean	Preserves class ratios; behaves closer to Accuracy
`samples`	Multi-label only. Average metrics per sample	For cases where each sample can have multiple labels

2. Comparing in Python 3.13 #

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python --version  # e.g. Python 3.13.0
pip install scikit-learn matplotlib

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from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = make_classification(
    n_samples=30_000,
    n_features=20,
    n_informative=6,
    weights=[0.85, 0.1, 0.05],  # Imbalanced classes
    random_state=42,
)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42
)

model = make_pipeline(
    StandardScaler(),
    LogisticRegression(max_iter=2000, multi_class="ovr"),
)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred, digits=3))
for avg in ["micro", "macro", "weighted"]:
    print(f"F1 ({avg}):", f1_score(y_test, y_pred, average=avg))

classification_report prints per-class metrics as well as macro avg, weighted avg, and micro avg, making it easy to compare the different strategies side by side.

3. Picking the right strategy #

micro – Best when you care about overall correctness and every prediction carries the same importance.
macro – Use when minority classes matter; it treats every class equally and penalises poor recall on rare labels.
weighted – Useful when you want to stay close to the real class distribution while still reporting Precision/Recall/F1.
samples – The default choice for multi-label tasks where each sample can have several ground-truth labels.

Takeaways #

The average parameter changes the meaning of the resulting metric drastically; match it with your task and business goal.
Remember: macro treats classes equally, micro focuses on overall ratios, weighted preserves class balance, and samples targets multi-label use cases.
scikit-learn lets you compute several averages in one go, so report multiple views to avoid misinterpreting model quality.