This article lists seven decorator tricks that can help you write cleaner code. Some of the examples shown are a perfect fit for using them in data science and data analysis workflows.

Machine Learning Mastery

04.10.2025

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data - MachineLearningMastery.com

In this article, you will learn how three widely used classifiers behave on class-imbalanced problems and the concrete tactics that make them work in practice.

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ROC AUC vs Precision-Recall for Imbalanced Data - MachineLearningMastery.com

7 Scikit-learn Tricks for Optimized Cross-Validation - MachineLearningMastery.com

7 Python Decorator Tricks to Write Cleaner Code - MachineLearningMastery.com

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data - MachineLearningMastery.com

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