Machine Learning Academy · レッスン

ランダムオーバーサンプリングとSMOTE

imbalanced-learnのRandomOverSamplerとSMOTEを適用して少数クラスをオーバーサンプリングし、適合率と再現率が改善するかを評価します。

「ランダムオーバーサンプリングとSMOTE」はCoddyKit上の無料Machine Learning Academyレッスンです。これはレッスン2/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはMachine Learning Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Machine Learning Academyコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Why Oversampling Helps Imbalanced Models

Many classifiers learn a biased decision boundary when training data is dominated by one class — they effectively ignore the minority class. Oversampling increases the representation of the minority class in the training data, forcing the model to pay more attention to it. Two popular approaches are random oversampling (duplicating existing minority samples) and SMOTE (synthesising new ones).

Random Oversampling: Duplicate Minority Samples

RandomOverSampler from the imbalanced-learn library randomly duplicates examples from the minority class until the desired class ratio is achieved. It is simple and effective, but the duplicate samples add no new information — the model may overfit to the repeated minority examples if not regularised carefully.

from imblearn.over_sampling import RandomOverSampler
from sklearn.datasets import make_classification
import numpy as np

X, y = make_classification(n_samples=1000, weights=[0.95, 0.05],
                            n_features=10, random_state=42)

print('Before resampling:', np.bincount(y))

ros = RandomOverSampler(random_state=42)
X_res, y_res = ros.fit_resample(X, y)

print('After resampling: ', np.bincount(y_res))

Installing imbalanced-learn

imbalanced-learn is a scikit-learn compatible library for handling class imbalance. Install it with pip install imbalanced-learn. It follows the sklearn API — samplers have fit_resample instead of fit_transform. It integrates with sklearn Pipelines via imblearn.pipeline.Pipeline, which is a drop-in replacement that supports samplers as pipeline steps.

# pip install imbalanced-learn

from imblearn import __version__ as imb_version
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import Pipeline as ImbPipeline

print('imbalanced-learn version:', imb_version)

SMOTE: Synthetic Minority Oversampling

SMOTE (Synthetic Minority Oversampling TEchnique) creates new synthetic minority examples rather than duplicating existing ones. For each minority sample, SMOTE finds its k nearest minority neighbours and creates new points along the line segments connecting them. These synthetic samples occupy the interior of the minority feature space, giving the classifier richer information about the minority class boundary.

SMOTE in Practice

Use imblearn.over_sampling.SMOTE. Key parameters: k_neighbors (default 5) — how many minority neighbours to consider. The sampling_strategy parameter controls the target ratio: 'auto' oversamples all minority classes to match the majority class count.

from imblearn.over_sampling import SMOTE
from sklearn.datasets import make_classification
import numpy as np

X, y = make_classification(n_samples=1000, weights=[0.95, 0.05],
                            n_features=10, random_state=42)

print('Before SMOTE:', np.bincount(y))

smote = SMOTE(k_neighbors=5, random_state=42)
X_res, y_res = smote.fit_resample(X, y)

print('After SMOTE:', np.bincount(y_res))
print('New shape:', X_res.shape)

Comparing Random Oversampling vs SMOTE

Random oversampling simply copies existing data and is faster; SMOTE synthesises new data and tends to generalise better. However, SMOTE can create unrealistic samples if the minority class is very sparse or if features have complex interactions. On highly non-linear problems, borderline-SMOTE or ADASYN (Adaptive Synthetic Sampling) may work better than standard SMOTE.

from imblearn.over_sampling import RandomOverSampler, SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np

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)

for name, sampler in [('None', None), ('ROS', RandomOverSampler(random_state=0)),
                      ('SMOTE', SMOTE(random_state=0))]:
    if sampler:
        X_r, y_r = sampler.fit_resample(X_train_s, y_train)
    else:
        X_r, y_r = X_train_s, y_train
    lr = LogisticRegression().fit(X_r, y_r)
    f1 = f1_score(y_test, lr.predict(X_test_s))
    print(f'{name:6s}: F1={f1:.4f}')

Using imbalanced-learn Pipeline

The critical rule: apply SMOTE only to the training fold, never to the test fold. The imblearn.pipeline.Pipeline enforces this automatically — SMOTE's fit_resample is called during fit but not during predict or transform. This is why you must use imblearn.pipeline.Pipeline, not sklearn's, when including a sampler.

from imblearn.pipeline import Pipeline as ImbPipeline
from imblearn.over_sampling import SMOTE
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_classification
import numpy as np

X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)

pipe = ImbPipeline([
    ('sc', StandardScaler()),
    ('smote', SMOTE(random_state=42)),  # applied only during fit
    ('lr', LogisticRegression())
])

scores = cross_val_score(pipe, X, y, cv=5, scoring='f1')
print(f'CV F1: {np.mean(scores):.4f} +/- {np.std(scores):.4f}')

Visualising SMOTE Samples

On a 2D dataset you can visualise where SMOTE places synthetic samples. The new points lie along line segments between existing minority samples in feature space. This confirms that SMOTE is interpolating within the minority class region — not extrapolating outside it.

import matplotlib.pyplot as plt
from imblearn.over_sampling import SMOTE
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=200, weights=[0.9, 0.1],
                            n_features=2, n_redundant=0,
                            n_informative=2, random_state=42)

smote = SMOTE(random_state=0)
X_res, y_res = smote.fit_resample(X, y)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
for ax, data, labels, title in [(ax1, X, y, 'Before SMOTE'),
                                  (ax2, X_res, y_res, 'After SMOTE')]:
    ax.scatter(data[labels==0, 0], data[labels==0, 1], label='Class 0', alpha=0.5)
    ax.scatter(data[labels==1, 0], data[labels==1, 1], label='Class 1', alpha=0.5)
    ax.set_title(title)
    ax.legend()
plt.tight_layout()
plt.show()

SMOTE Variants: Borderline and ADASYN

Borderline-SMOTE generates synthetic samples only near the decision boundary (borderline minority samples closest to the majority class), focusing the model's attention where classification is hardest. ADASYN adaptively generates more samples in regions where the class is densest, adding proportionally more synthetic examples where the classifier struggles most.

from imblearn.over_sampling import BorderlineSMOTE, ADASYN
from sklearn.datasets import make_classification
import numpy as np

X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)

print('Before:', np.bincount(y))

bs = BorderlineSMOTE(random_state=0)
X_bs, y_bs = bs.fit_resample(X, y)
print('After BorderlineSMOTE:', np.bincount(y_bs))

adas = ADASYN(random_state=0)
X_ad, y_ad = adas.fit_resample(X, y)
print('After ADASYN:', np.bincount(y_ad))

Combining SMOTE with Undersampling (SMOTEENN)

SMOTE alone can introduce noisy samples near the majority-class boundary. SMOTEENN combines SMOTE with Edited Nearest Neighbours (ENN) cleaning: after SMOTE upsamples, ENN removes misclassified samples from both classes near the boundary. The result is a cleaner dataset with both oversampled minority and cleaned majority regions.

from imblearn.combine import SMOTEENN
from sklearn.datasets import make_classification
import numpy as np

X, y = make_classification(n_samples=1000, weights=[0.95, 0.05], random_state=42)
print('Before:', np.bincount(y))

smoteenn = SMOTEENN(random_state=42)
X_res, y_res = smoteenn.fit_resample(X, y)
print('After SMOTEENN:', np.bincount(y_res))

Evaluating SMOTE: Precision-Recall Trade-Off

After applying oversampling, always evaluate with metrics sensitive to the minority class — F1-score, ROC-AUC, or PR-AUC. SMOTE typically improves recall (fewer missed positives) but may reduce precision (more false alarms). Use the precision-recall curve to find the threshold that gives your desired operating point for the specific business cost of false positives vs false negatives.

Quick Check

Test your understanding of oversampling and SMOTE from this lesson.

Lesson Recap

In this lesson you learned: RandomOverSampler duplicates minority examples — simple but risks overfitting to repeated data, SMOTE synthesises new minority samples by interpolating between existing ones, giving the classifier richer information, and imblearn.pipeline.Pipeline ensures SMOTE is applied only to training folds, preventing data leakage. Next up we explore undersampling techniques that reduce the majority class instead of growing the minority.

よくある質問

「ランダムオーバーサンプリングとSMOTE」レッスンは無料ですか？

はい。「ランダムオーバーサンプリングとSMOTE」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Machine Learning Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Machine Learning Academyコースには全4レッスンが含まれています。

「ランダムオーバーサンプリングとSMOTE」で何を学びますか？

imbalanced-learnのRandomOverSamplerとSMOTEを適用して少数クラスをオーバーサンプリングし、適合率と再現率が改善するかを評価します。ブラウザで直接実行するハンズオンコードでMachine Learning Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

Machine Learning Academyを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのMachine Learning Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン2/4です。

「ランダムオーバーサンプリングとSMOTE」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このMachine Learning Academyレッスンでコードを書いて実行できますか？

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