How can you evaluate Machine Learning models with imbalanced data?

2 What are some evaluation metrics for imbalanced data?

The most common evaluation metrics for classification problems are accuracy, precision, recall, and F1-score. However, these metrics may not be suitable for imbalanced data, as they can be misleading or insensitive to the minority class. For instance, accuracy can be high even if the model predicts the majority class for all instances, while precision and recall can vary depending on the threshold or cutoff value. F1-score is a harmonic mean of precision and recall, but it may not capture the trade-off between them adequately. Alternative evaluation metrics for imbalanced data include ROC curve and AUC, which plots the true positive rate (TPR) versus the false positive rate (FPR) for different threshold values and measures the area under the curve respectively. A higher AUC indicates a better model that can distinguish between the classes. Precision-recall curve and average precision is another metric that plots the precision versus the recall for different threshold values and measures the area under the curve respectively. A higher average precision indicates a better model that can predict the minority class accurately. Additionally, Cohen's kappa is a measure of agreement between the model's predictions and the actual labels, adjusted for chance. A higher kappa indicates a better model that can predict both classes reliably.

4 How can you adjust the model?

Another way to deal with imbalanced data is to adjust the model by modifying the learning algorithm or the objective function. Class weights, cost-sensitive learning, and ensemble learning are some common methods for adjusting the model. Class weights assigns different weights to different classes, based on their frequency or importance. Cost-sensitive learning assigns different costs to different types of errors, such as false positives and false negatives. Ensemble learning combines multiple models, such as decision trees or neural networks, to create a more robust and accurate model. However, these methods may require manual tuning, estimating costs from domain knowledge or business objectives, or increasing complexity and computational cost of the model, respectively.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?