Imbalanced Classification Master Class in Python

Welcome to Imbalanced Classification Master Class in Python.

Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification is those classification tasks where the distribution of examples across the classes is not equal. Typically the class distribution is severely skewed so that for each example in the minority class, there may be one hundred or even one thousand examples in the majority class. Practical imbalanced classification requires the use of a suite of specialized techniques, data preparation techniques, learning algorithms, and performance metrics.

Let’s discuss what you’ll learn in this course.

The challenge and intuitions for imbalanced classification datasets.

How to choose an appropriate performance metric for evaluating models for imbalanced classification.

How to appropriately stratify an imbalanced dataset when splitting into train and test sets and when using k–fold cross–validation.

How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of standard machine learning models.

How algorithms from the field of cost–sensitive learning can be used for imbalanced classification.

How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account.

How to tune the threshold when interpreting predicted probabilities as class labels.