Aumentare i rilevamenti corretti con il ricampionamento dei dati

Rilevamento delle frodi in Python

Charlotte Werger

Data Scientist

Undersampling

Oversampling

Oversampling in 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

Quale metodo di ricampionamento usare?

Random Under Sampling (RUS): scarta dati, efficiente computazionalmente
Random Over Sampling (ROS): semplice e diretto, ma allena il modello su molti duplicati
Synthetic Minority Oversampling Technique (SMOTE): dataset più realistico e sofisticato, ma alleni su dati “finti”

Quando usare i metodi di ricampionamento

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))

Passiamo alla pratica!

Rilevamento delle frodi in Python