Predictive Analytics: Using SMOTE, Model Explanations, & Hyperparameter Tuning

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Machine learning (ML) models can struggle with training themselves to identify failures if the dataset's number of machine failures is too low. This is a common problem that occurs when predicting very rare occurrences. Thankfully, oversampling techniques exist to mitigate such issues. In this course, learn how to use SMOTE, a widely used technique to make datasets more balanced. Next, explore model explanations, a feature of Azure Machine Learning. Finally, practice performing hyperparameter tuning by trying different model configurations to see which yields the best performance. Upon completion, you'll be able to improve the performance of a failure detection model, generate records of minority classes, and perform hyperparameter tuning.