Imbalanced Data 😎 How you doin'? #shorts

🔥 Imbalanced data refers to a situation where the number of examples in different classes or categories is not equal, with one class being much smaller in size compared to the others. A classic example is Loan Prediction Problem, where Training Datasets generally have more "good loans" than "bad loans".

🔥 Implications of imbalanced data include:
----- Biased model performance,
----- Lower predictive accuracy for the minority class,
----- Difficulty in detecting rare events, and
----- Challenges in decision-making due to an imbalanced distribution

Further, imbalanced data can lead to skewed results, limited generalization, and the potential for overlooking important patterns or insights associated with the minority class. Addressing imbalanced data is crucial for building reliable and fair predictive models.

🔥 Here's a set of techniques for tackling imbalanced data:
----- Resampling Techniques
----- Class Weights
----- Right Metric to Optimize
----- Threshold Adjustment
----- Focal Loss

🔥 Among the popular techniques, there are a couple:
1. Resampling Techniques are two basically:
----- Undersampling: Reduce instances in the majority group.
----- Oversampling: Increase the number of bad loans, using techniques like SMOTE - where we create synthetic data by augmenting existing data points.

2. Class Weights
----- Certain algorithms have a class_weight hyperparameter to handle imbalance.

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