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Deteksi Kecurangan di Python

Charlotte Werger

Data Scientist

Undersampling

Oversampling

Oversampling di Python

from imblearn.over_sampling import RandomOverSampler

method = RandomOverSampler()
X_resampled, y_resampled = method.fit_resample(X, y)

compare_plots(X_resampled, y_resampled, X, y)

Synthetic Minority Oversampling Technique (SMOTE)

¹ https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets

Metode resampling mana yang digunakan?

Random Under Sampling (RUS): buang sebagian data, efisien secara komputasi
Random Over Sampling (ROS): mudah dan langsung, tetapi banyak duplikat saat melatih model
Synthetic Minority Oversampling Technique (SMOTE): lebih canggih dan realistis, tetapi melatih pada data "palsu"

Kapan menggunakan metode resampling

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Define resampling method and split into train and test
method = SMOTE()
X_train, X_test, y_train, y_test = train_test_split(X, y,
 train_size=0.8, random_state=0)

# Apply resampling to the training data only
X_resampled, y_resampled = method.fit_resample(X_train, y_train)

# Continue fitting the model and obtain predictions
model = LogisticRegression()
model.fit(X_resampled, y_resampled)

# Get your performance metrics
predicted = model.predict(X_test)
print (classification_report(y_test, predicted))

Ayo berlatih!

Deteksi Kecurangan di Python