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Python 3
Evaluating Model Performance

Evaluating Model Performance

Evaluating Model Performance
Precision Score
Recall Score
F1 Score
ROC-AUC Score

Evaluating Model Performance

Calculate the model’s accuracy on both the training and testing datasets.

Training Accuracy

training_accuracy = accuracy_score(

Y_train,

training_predictions

)

print(training_accuracy)

Testing Accuracy

testing_accuracy = accuracy_score(

Y_test,

testing_predictions

)

print(testing_accuracy)

Precision Score

precision = precision_score(

Y_test,

testing_predictions

)

print(precision)

Recall Score

recall = recall_score(

Y_test,

testing_predictions

)

print(recall)

F1 Score

f1 = f1_score(

Y_test,

testing_predictions

)

print(f1)

ROC-AUC Score

roc = roc_auc_score(

Y_test,

testing_predictions

)

print(roc)

Explanation

Accuracy measures the proportion of correctly classified transactions.

However, because the dataset is highly imbalanced, additional evaluation metrics such as precision, recall, and F1-score should also be considered.

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Evaluating Model Performance

Contents

Evaluating Model Performance

Training Accuracy

Testing Accuracy

Precision Score

Recall Score

F1 Score

ROC-AUC Score

Explanation

Credit Card Fraud Detection for Beginners using Data Science

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