Implementation:Scikit learn contrib Imbalanced learn classification report imbalanced

Knowledge Sources	imbalanced-learn imbalanced-learn Docs
Domains	Machine_Learning, Model_Evaluation, Imbalanced_Learning
Last Updated	2026-02-09 03:00 GMT

Overview

Concrete tool for generating a comprehensive imbalanced classification report provided by the imbalanced-learn library.

Description

The classification_report_imbalanced function generates a text or dict report with per-class precision, recall, specificity, f1, geometric mean, IBA, and support. Supports custom target names, label subsets, and dict output mode.

Usage

Import this function as a replacement for sklearn's classification_report when working with imbalanced datasets.

Code Reference

Source Location

• Repository: imbalanced-learn
• File: imblearn/metrics/_classification.py
• Lines: L865-1143

Signature

def classification_report_imbalanced(
    y_true,
    y_pred,
    *,
    labels=None,
    target_names=None,
    sample_weight=None,
    digits=2,
    alpha=0.1,
    output_dict=False,
    zero_division="warn",
):
    """
    Args:
        y_true: array-like - Ground truth labels.
        y_pred: array-like - Predicted labels.
        labels: array-like or None - Label indices to include.
        target_names: list of str or None - Display names for labels.
        sample_weight: array-like or None - Sample weights.
        digits: int - Decimal precision (default: 2).
        alpha: float - IBA alpha parameter (default: 0.1).
        output_dict: bool - Return dict instead of string (default: False).
        zero_division: 'warn' or 0 or 1 - Zero division behavior.
    Returns:
        str or dict - Formatted classification report.
    """

Import

from imblearn.metrics import classification_report_imbalanced

I/O Contract

Inputs

Name	Type	Required	Description
y_true	array-like of shape (n_samples,)	Yes	Ground truth labels
y_pred	array-like of shape (n_samples,)	Yes	Predicted labels
target_names	list of str or None	No	Display names per class
output_dict	bool	No	Return dict (default: False, returns string)

Outputs

Name	Type	Description
report	str or dict	Report with pre, rec, spe, f1, geo, iba, sup per class plus averages

Usage Examples

from imblearn.metrics import classification_report_imbalanced

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ["class 0", "class 1", "class 2"]

print(classification_report_imbalanced(y_true, y_pred, target_names=target_names))

# As dictionary
report_dict = classification_report_imbalanced(
    y_true, y_pred, output_dict=True
)

Related Pages

Implements Principle

• Principle:Scikit_learn_contrib_Imbalanced_learn_Imbalanced_Classification_Reporting

Requires Environment

• Environment:Scikit_learn_contrib_Imbalanced_learn_Python_Scikit_learn