In this video, you will be learning about how you can handle imbalanced datasets. Particularly, your class labels for your classification model is imbalanced (one class is significantly larger than the other which essentially gives rise to a majority class and minority class). Here, we will use the imbalanced-learn Python library to perform random undersampling and random oversampling so that you can address this issue of imbalanced datasets.
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How to handle imbalanced datasets in Python
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imbalanced classimbalanced classificationdata imbalancedata imbalance machine learningmachine learning class imbalancemachine learning class balancingmachine learning undersamplingmachine learning oversamplingpython class balancepython class imbalancebalancing data for machine learningclass imbalanceclass balancingclass balancing pythonclass imbalance pythonimbalanced-learnimbalanced learnimbalanced learningimbalanced data
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