How to Handle Imbalanced Data for Machine Learning in Python

In dealing with classification problems in machine learning, handling imbalanced data is crucial as it can bias model performance towards the majority class, leading to misleading accuracy metrics. The text explains the implications of class imbalance, such as biased learning and misleading accuracy, and explores scenarios where such imbalances are expected, like in rare disease detection or fraud analysis. It discusses evaluation metrics affected by imbalance, such as accuracy, precision, recall, and the F1 score, and those not affected, like confusion matrices and AUC/ROC curves. Techniques to address imbalance include resampling methods like oversampling and undersampling, each with its pros and cons, and ensemble learning methods such as Random Forest, which inherently address class imbalance through bootstrapped sampling and random feature selection. The text underscores the importance of choosing appropriate strategies based on the dataset's characteristics and highlights potential issues like overfitting, suggesting hyperparameter tuning as a remedy.