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

Predictive Analytics 2022    |    Intermediate
  • 11 Videos | 1h 15m 22s
  • Includes Assessment
  • Earns a Badge
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.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    use SMOTE to improve the performance of a machine failure prediction model
    view important machine failure prediction attributes
    observe model explanations and performance metrics for machine failure prediction models
    standardize data using a component
    view the effects of standardizing machine failure data
  • create and configure a decision forest model for machine failure prediction
    predict machine failure using decision forests and compare the performance to the logistic regression model
    compare the performance of the logistic regression and decision forest models
    perform hyperparameter tuning on a machine failure prediction model and view the results
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 57s
    UP NEXT
  • Playable
    2. 
    Oversampling Machine Failure Incidents Using SMOTE
    8m 38s
  • Locked
    3. 
    Viewing Important Model Attributes
    8m 7s
  • Locked
    4. 
    Observing Model Explanations and Performance Metrics
    5m 34s
  • Locked
    5. 
    Standardizing Machine Failure Data Using a Component
    7m 45s
  • Locked
    6. 
    Viewing Machine Failure Data Standardization Results
    5m 52s
  • Locked
    7. 
    Creating and Configuring a Decision Tree Forest
    9m 1s
  • Locked
    8. 
    Predicting Machine Failure Using Decision Forests
    8m 15s
  • Locked
    9. 
    Comparing Model Performance
    5m 14s
  • Locked
    10. 
    Performing Hyperparameter Tuning on a Model
    10m 9s
  • Locked
    11. 
    Course Summary
    4m 51s

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