Predictive Analytics: Using SMOTE Model Explanations & Hyperparameter Tuning

Machine learning (ML) models can struggle with training themselves to identify failures if the datasets 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 youll be able to improve the performance of a failure detection model generate records of minority classes and perform hyperparameter tuning.