1. Azure Machine Learning Algorithm Accuracy Enhancement, Tips and Tricks
7/20/ :17 PM
THR3078
Azure Machine Learning Algorithm Accuracy Enhancement, Tips and Tricks
Leila Etaati
AI MVP, PhD, Consultant
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Leila Etaati Microsoft AI MVP,
PhD, Senior Consultant, Trainer and Data Scientist.
International speaker in Microsoft Ignite USA 2017, Microsoft Insight Summit 2017, PASS Summit 2017, Microsoft NZ Ignite 2016, PASS BA, PASS24H, SQLRally, SQL Saturday in Oregon, Vienna, Auckland, Melbourne, Sydney, Brisbane.

3. Azure ML

4. Machine Learning Process
What is Business Problem
Collecting & Cleaning Data
Choose Model &Parameters
Evaluate Model
Test Model
Train Model

5. What is Business Problem

6. What is Business Problem
Prediction
Predictive Analytics
Grouping
Descriptive Analysis
Find Unusual Data Point
Descriptive-Anomaly Detection

7. What is Business Problem -Predictive Analytics
Predict a Group
Predict a Value

8. What is Business Problem - Descriptive Analytics

9. What is Business Problem - Anomaly Detection

10. Collecting & Cleaning Data

11. Collecting & Cleaning Data-Clean Data
7/20/ :17 PM
Length
Weight
height
area
2000
20k
1.1 k
0.45
Bring Data In one Scale
Length
Weight
height
area
0.2
0.4
0.67
0.45
Remove Outliers
5,2
22,25,28,25,27,30,31,22
60,70
Gender
Female
Male
Female 1
Male 1
Convert to Indicator Values
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. Collecting & Cleaning Data-Clean Data

13. Collecting & Cleaning Data- Feature Selection
Gender
Age No
Children
Charges
Region
BMI
Smoker

14. Demo-Feature Selection
7/20/ :17 PM
Demo-Feature Selection
Leila Etaati
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

15. Collecting & Cleaning Data- Cross Validation
7/20/ :17 PM
Collecting & Cleaning Data- Cross Validation
Data
Data
Data
Data
Data
Data for Test
Data for Train
Test
Train
Test
Test
Test
Test
Apply Model
Apply Model
Apply Model
Apply Model
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. Demo-Cross Validation
7/20/ :17 PM
Demo-Cross Validation
Leila Etaati
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

17. Choose Model & Parameters

18. Choose Model & Parameters: Data Linearity

19. Two main problem: Over fit and Under fit

20. Two main problem: Over fit and Under fit

21. Choose Model (Parameters):Parameters Tuning
Decision Tree, Forrest, ..
Depth of tree, Number of Decision Tree
Clustering- K-mean, Number of Clusters

22. Demo-Tune Model Hyperparameters
7/20/ :17 PM
Demo-Tune Model Hyperparameters
Leila Etaati
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

23. Machine Learning Process
What is Business Problem
Collecting & Cleaning Data
Choose Model &Parameters
Evaluate Model
Test Model
Train Model

24. Please evaluate this session
Tech Ready 15
7/20/2018
Please evaluate this session
From your
Please expand notes window at bottom of slide and read. Then Delete this text box.
PC or tablet: visit MyIgnite
Phone: download and use the Microsoft Ignite mobile app
Your input is important!
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.