Machine Learning with Imbalanced Data - Part 2 (Cost-sensitive Learning)

Описание к видео Machine Learning with Imbalanced Data - Part 2 (Cost-sensitive Learning)

In this video, we discuss the class imbalance problem and several strategies to address this problem. Existing methods can be divided into data-level preprocessing methods (resampling), cost-sensitive learning, and ensemble learning. The main focus of this video is cost-sensitive little with an emphasis on logistic regression classifier. We use the scikit-learn implementation of logistic regression to improve the performance of logistic regression on the thyroid data set. We use precision and recall scores as evaluation metrics in Python.

#ImbalancedData #CostSensitiveLearning #Classification

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