불균형 감지하기: 클래스 분포와 기준선의 함정
학습자는 클래스 빈도를 계산하고, 불균형 데이터셋에서 단순 분류기의 함정을 드러내며, 이 경우 정확도가 오해를 불러일으키는 지표임을 확인합니다.
불균형 감지하기: 클래스 분포와 기준선의 함정은(는) CoddyKit의 무료 Machine Learning Academy 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Machine Learning Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Machine Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
What Is Class Imbalance?
Class imbalance occurs when one class dominates the dataset. In fraud detection, fraudulent transactions may be 0.1% of all records; in medical diagnosis, a rare disease may affect 1 in 1000 patients. A model that always predicts the majority class achieves 99.9% accuracy while completely ignoring the rare, critical class — making accuracy a dangerously misleading metric in these scenarios.
Measuring Class Distribution
Before training any model, inspect the class distribution with pd.Series(y).value_counts() or np.bincount(y). A useful summary statistic is the imbalance ratio — the ratio of majority to minority class count. Ratios above 10:1 are considered imbalanced; above 100:1 are severely imbalanced and require specialised techniques.
import numpy as np
import pandas as pd
# Simulate imbalanced binary classification dataset
np.random.seed(0)
y = np.array([0] * 950 + [1] * 50) # 95% negative, 5% positive
counts = pd.Series(y).value_counts()
print('Class counts:\n', counts)
print()
print('Class proportions:\n', counts / len(y))
print()
print('Imbalance ratio:', counts[0] / counts[1])The Accuracy Trap with Imbalanced Data
On a dataset with 95% negative examples, a model that always predicts the majority class (negative) achieves 95% accuracy without learning anything useful. This is the accuracy trap. The model has zero ability to detect positive cases (the class you actually care about), yet its accuracy looks impressive. This is why imbalanced datasets demand metrics beyond accuracy.
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score, classification_report
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05],
random_state=42, n_features=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# Always predict majority class
dummy = DummyClassifier(strategy='most_frequent')
dummy.fit(X_train, y_train)
y_pred = dummy.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred).round(4))
print()
print(classification_report(y_test, y_pred))Why the Dummy Classifier Baseline Matters
The DummyClassifier with strategy='most_frequent' is the minimum acceptable baseline. Any real model must beat it meaningfully — not just on accuracy but on the metrics that matter (precision, recall, F1, or AUC-ROC). If your real model is only marginally better than the dummy, it has likely learned to ignore the minority class too.
from sklearn.linear_model import LogisticRegression
from sklearn.dummy import DummyClassifier
from sklearn.metrics import classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
X_train_s = StandardScaler().fit_transform(X_train)
X_test_s = StandardScaler().fit_transform(X_test)
lr = LogisticRegression().fit(X_train_s, y_train)
print('--- Logistic Regression ---')
print(classification_report(y_test, lr.predict(X_test_s)))Precision and Recall on Imbalanced Data
For imbalanced problems, the two most informative metrics are: Precision = TP / (TP + FP) — of all predicted positives, how many were actually positive? Recall = TP / (TP + FN) — of all actual positives, how many did the model catch? In fraud detection, recall is paramount (don't miss fraud); in spam filtering, precision matters (don't misclassify legitimate emails).
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
X_train_s = StandardScaler().fit_transform(X_train)
X_test_s = StandardScaler().fit_transform(X_test)
lr = LogisticRegression().fit(X_train_s, y_train)
y_pred = lr.predict(X_test_s)
print(f'Precision: {precision_score(y_test, y_pred):.4f}')
print(f'Recall: {recall_score(y_test, y_pred):.4f}')
print(f'F1-Score: {f1_score(y_test, y_pred):.4f}')ROC-AUC: Threshold-Agnostic Evaluation
ROC-AUC evaluates the model at all possible decision thresholds and measures the area under the resulting curve. An AUC of 0.5 is random; 1.0 is perfect; 0.9+ is excellent. Unlike accuracy, AUC is not affected by class imbalance because it evaluates the model's ranking ability rather than a fixed threshold prediction. AUC is the recommended primary metric for most imbalanced binary classification tasks.
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
sc = StandardScaler()
X_train_s = sc.fit_transform(X_train)
X_test_s = sc.transform(X_test)
lr = LogisticRegression().fit(X_train_s, y_train)
proba = lr.predict_proba(X_test_s)[:, 1]
print('ROC-AUC:', roc_auc_score(y_test, proba).round(4))Visualising Class Imbalance
A simple bar chart of class frequencies immediately communicates imbalance to stakeholders. Include it in your exploratory data analysis notebook as standard practice. If you are working with a Pandas DataFrame, also check for hidden imbalance caused by data collection bias rather than actual rarity in the real world.
import matplotlib.pyplot as plt
import numpy as np
y = np.array([0] * 950 + [1] * 50)
classes, counts = np.unique(y, return_counts=True)
plt.bar(['Negative (0)', 'Positive (1)'], counts, color=['#2196F3', '#F44336'])
plt.ylabel('Count')
plt.title('Class Distribution (95:5 imbalance)')
for i, c in enumerate(counts):
plt.text(i, c + 5, str(c), ha='center', fontweight='bold')
plt.show()Stratified Splitting to Preserve Ratios
When splitting an imbalanced dataset, use stratify=y in train_test_split. Without stratification, random chance might put all minority-class samples in one split, making training or evaluation meaningless. Stratified splitting ensures both train and test sets have the same class ratio as the original dataset.
from sklearn.model_selection import train_test_split
import numpy as np
y = np.array([0] * 950 + [1] * 50)
X = np.random.randn(1000, 5)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, stratify=y, random_state=42
)
print('Train class ratio:', (y_train == 1).mean().round(4))
print('Test class ratio:', (y_test == 1).mean().round(4))
print('Expected ratio:', (y == 1).mean().round(4))Stratified K-Fold for Cross-Validation
Similarly, use StratifiedKFold (or simply pass cv=5 to cross_val_score — scikit-learn uses StratifiedKFold automatically for classifiers) to ensure each fold maintains the original class ratio. A regular KFold with small minority classes can create folds with zero positive examples, causing errors or misleading metrics.
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy as np
y = np.array([0] * 950 + [1] * 50)
X = np.random.randn(1000, 5)
pipe = Pipeline([('sc', StandardScaler()), ('lr', LogisticRegression())])
skf = StratifiedKFold(n_splits=5)
scores = cross_val_score(pipe, X, y, cv=skf, scoring='roc_auc')
print(f'Stratified CV AUC: {np.mean(scores):.4f} +/- {np.std(scores):.4f}')The Precision-Recall Curve
For highly imbalanced data, the precision-recall (PR) curve is often more informative than the ROC curve. The PR curve shows precision on the y-axis and recall on the x-axis at all thresholds. The area under the PR curve (AP score) ranges from 0 to 1 and is sensitive to the minority-class performance in a way that ROC-AUC can mask when the majority class is overwhelming.
from sklearn.metrics import precision_recall_curve, average_precision_score
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
sc = StandardScaler()
X_train_s = sc.fit_transform(X_train)
X_test_s = sc.transform(X_test)
lr = LogisticRegression().fit(X_train_s, y_train)
proba = lr.predict_proba(X_test_s)[:, 1]
precision, recall, _ = precision_recall_curve(y_test, proba)
ap = average_precision_score(y_test, proba)
plt.plot(recall, precision, label=f'AP={ap:.3f}')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.legend()
plt.show()Documenting Imbalance Before Modelling
Best practice: create an imbalance report at the start of every classification project. Record class counts, ratios, and the baseline dummy accuracy. This sets expectations and forces the team to agree on the right success metric before any model is trained. A fraud detection model with 95% accuracy is usually worthless — your stakeholders need to know this upfront.
import numpy as np
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score, roc_auc_score
y = np.array([0] * 950 + [1] * 50)
X = np.random.randn(1000, 5)
dummy = DummyClassifier(strategy='most_frequent').fit(X, y)
dummy_acc = accuracy_score(y, dummy.predict(X))
dummy_auc = roc_auc_score(y, dummy.predict_proba(X)[:, 1])
print('=== Imbalance Report ===')
print(f'Class 0: {(y==0).sum()} ({(y==0).mean():.1%})')
print(f'Class 1: {(y==1).sum()} ({(y==1).mean():.1%})')
print(f'Imbalance ratio: {(y==0).sum()/(y==1).sum():.0f}:1')
print(f'Dummy accuracy: {dummy_acc:.4f}')
print(f'Dummy AUC: {dummy_auc:.4f}')
print('Recommended metric: ROC-AUC or Precision-Recall AUC')Quick Check
Test your understanding of class imbalance and baseline pitfalls from this lesson.
Lesson Recap
In this lesson you learned: class imbalance makes accuracy a misleading metric — a model predicting only the majority class can achieve very high accuracy, always compare against a DummyClassifier baseline to ensure your model is learning something beyond the trivial, and use ROC-AUC or precision-recall AUC as primary metrics for imbalanced datasets. Next up we apply SMOTE and random oversampling to upsample the minority class.
자주 묻는 질문
“불균형 감지하기: 클래스 분포와 기준선의 함정” 강의는 무료인가요?
네 — “불균형 감지하기: 클래스 분포와 기준선의 함정” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Machine Learning Academy 강의 전체를 잠금 해제할 수 있습니다. Machine Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.
“불균형 감지하기: 클래스 분포와 기준선의 함정”에서 뭘 배우나요?
학습자는 클래스 빈도를 계산하고, 불균형 데이터셋에서 단순 분류기의 함정을 드러내며, 이 경우 정확도가 오해를 불러일으키는 지표임을 확인합니다. 브라우저에서 직접 실행하는 실습 코드로 Machine Learning Academy을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
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이 강의의 모든 강의
- 불균형 감지하기: 클래스 분포와 기준선의 함정
- 무작위 오버샘플링과 SMOTE
- 무작위 언더샘플링과 ClusterCentroids
- 클래스 가중치와 임계값 조정