Machine Learning with Imbalanced Data - Part 3 (Over-sampling, SMOTE, and Imbalanced-learn)

Описание к видео Machine Learning with Imbalanced Data - Part 3 (Over-sampling, SMOTE, and Imbalanced-learn)

In this video, we discuss the class imbalance problem and how to use over-sampling methods to address this problem. We use the thyroid data set and the logistic regression classifier to train binary classifiers on the original data set and the preprocessed data. We discuss uniform sampling with replacement and SMOTE (Synthetic Minority Over-sampling Technique) using imbalanced-learn library (https://imbalanced-learn.org/stable/) in Python. We plot the confusion matrix for each case to demonstrate the effectiveness of resampling methods.

#ImbalancedData #Resampling #SMOTE

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