Step-by-Step Python Implementation with Dr. Data Science, Cost-sensitive Learning and Google Colab

In this video, I provide a step-by-step Python implementation of the cost-sensitive learning strategy, which is a common technique for handling the class imbalance problem. This video uses the logistic regression classifier and a data set from Imbalanced-learn (imported as imblearn), an open-source, MIT-licensed library relying on scikit-learn. I show how to alter the optimization problem to improve the performance of the trained classifier using several evaluation metrics, including confusion matrix, precision, and recall scores. This video uses Google Colaboratory or Google Colab, which requires minimal configurations. This video is useful for those who are interested in machine learning and data science for solving real-world problems, providing a simple and easy-to-understand tutorial.

Machine Learning with Imbalanced Data Link:    • Machine Learning with Imbalanced Data...  

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