Review of classification methods

Fraud Detection in Python

Charlotte Werger

Data Scientist

What is classification?

Goal of classification: Use known fraud cases to train a model to recognize new fraud cases

Examples:

Email spam/Not spam
Transaction online fraudulent: Yes/No
Tumor Malignant/Benign?

Variable to predict: $y \in {0,1} $

0: Negative class ("majority" normal cases)

1: Positive class ("minority" fraud cases)

Classification methods commonly used for fraud detection

Logistic regression

Classification methods commonly used for fraud detection

Neural network

Classification methods commonly used for fraud detection

Decision trees
Random forests

Decision trees and random forests

Random forests are a collection of trees on random subsets of features

Random forests for fraud detection

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(random_state=42)

model.fit(X_train, y_train)

predicted = model.predict(X_test)

print (metrics.accuracy_score(y_test, predicted))

0.991324200913242

Let's practice!

Fraud Detection in Python