Predictive Analytics: Using SMOTE, Model Explanations, & Hyperparameter Tuning
Predictive Analytics
| 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
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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
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1m 57s
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8m 38s
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3. Viewing Important Model Attributes8m 7s
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4. Observing Model Explanations and Performance Metrics5m 34s
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5. Standardizing Machine Failure Data Using a Component7m 45s
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6. Viewing Machine Failure Data Standardization Results5m 52s
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7. Creating and Configuring a Decision Tree Forest9m 1s
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8. Predicting Machine Failure Using Decision Forests8m 15s
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9. Comparing Model Performance5m 14s
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10. Performing Hyperparameter Tuning on a Model10m 9s
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11. Course Summary4m 51s
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