OML feature highlight: New OML Notebook templates for ML Feature Extraction

In this weekly Office Hours for Oracle Machine Learning on Autonomous Database, we introduced the latest Notebook templates for Machine Learning Feature Extraction problems. This was a follow-along Session, since the OML Notebook templates are available to any Autonomous Database tenancy, and people were able to run it while we demonstrated it.

Transcript

1. OML feature highlight: New OML Notebook
   templates for ML Feature Extraction
   OML Office Hours
   Marcos Arancibia, Senior Principal Product Manager, Machine Learning
   Supported by Mark Hornick and Sherry LaMonica
   Senior Director, Product Management, Data Science and Machine Learning
   Move the Algorithms; Not the Data!
   Copyright © 2022, Oracle and/or its affiliates.
   This Session will
   be Recorded
   

   View Slide

2. • Upcoming Sessions
   • Follow-along OML Notebooks ML Feature Extraction demos
   • Q&A
   Topics for today
   Copyright © 2022, Oracle and/or its affiliates
   2
   

   View Slide

3. We will begin a Series of Follow-along reviews of the Example Template notebooks every week, with
   one subject per week. These will be Hands-on if you have access to any Autonomous Database, even
   the Always-Free one.
   • Classification - done
   • Regression - done
   • Clustering
   • Feature Extraction I – Dimensionality Reduction
   • Feature Extraction II - Explicit Semantic Analysis
   • Time Series
   Upcoming Sessions
   Copyright © 2022, Oracle and/or its affiliates
   3
   

   View Slide

4. • Plus a Hands-on Lab Session on OML4Py in
   Portuguese to be scheduled on February 11,
   2022 at 8AM Pacific, 11Am Eastern, 1PM Brazil
   time.
   Upcoming Sessions
   Copyright © 2022, Oracle and/or its affiliates
   4
   

   View Slide

5. Algorithms
   Some of the methods for Feature Extraction include:
   - Attribute importance using Minimum Description Length
   - Feature Extraction methods that use a transformation/translation/rotation of the original attribute
   axis, or a decomposition of the original variables into a set of matrices, like:
   - (PCA) Principal Component Analysis,
   - (SVD) Singular Value Decomposition,
   - (NMF) Non-Negative Matrix Factorization,
   - (EM) Expectation-Maximization,
   - CUR Matrix Decomposition,
   - Explicit Semantic Analysis for NLP and information retrieval.
   Using transformations or simply the exclusion of variables/columns with lower relationship with the
   target is helpful when building predictive models with machine learning, and because good data
   preparation is usually 90% of the work, Feature Extraction might be a key element to assist in a better
   model.
   Feature Extraction
   Copyright © 2022, Oracle and/or its affiliates
   5
   

   View Slide

6. • Compute the relative importance of predictor variables for predicting
   a response (target) variable
   • Gain insight into relevance of variables to guide manual variable selection
   or reduction, with the goal to reduce predictive model build time and/or
   improve model accuracy
   • Attribute Importance uses a Minimum Description Length (MDL) based
   algorithm that ranks the relative importance of predictor variables in
   predicting a specified response (target) variable
   • Pairwise only – each predictor with the target
   • Supports categorical target (classification) and numeric target (regression)
   Attribute Importance
   Copyright © 2022, Oracle and/or its affiliates
   6
   

   View Slide

7. • Includes Auto Data Preparation (Normalization, binning)
   • Can allow or ignore missing values
   • Supports partitioned model builds
   • Does NOT have a "Scoring" action, only presents the attribute importance
   so the user can explore the results
   • Returns a relative metric indicating how much the variable contributes to
   predicting the target
   • Values > 0 contribute to prediction
   • Values <= do not contribute or add noise
   OML Attribute Importance
   Copyright © 2022, Oracle and/or its affiliates
   7
   

   View Slide

8. • Feature extraction algorithm
   • Orthogonal linear transformations capture the underlying variance of data
   by decomposing a rectangular matrix into three matrices: U, D and V
   • Matrix D is a diagonal matrix and its singular values reflect the amount of
   data variance captured by the singular vectors
   Singular Value Decomposition - SVD
   Copyright © 2022, Oracle and/or its affiliates
   8
   

   View Slide

9. • Supports narrow data via Tall and Skinny solvers
   • Supports wide data via stochastic solvers
   • Provides Eigen Solvers for faster analysis with sparse data
   • Provides traditional SVD for more stable results
   • Provides PCA (Principal Components Analysis) scores
   Oracle Machine Learning SVD implementation
   Copyright © 2022, Oracle and/or its affiliates
   9
   

   View Slide

10. • State-of-the-art algorithm for Feature Extraction
    • Dimensionality reduction technique
    • Creates new features of existing attributes (in contrast to Attribute
    Importance, which reduces attributes by taking a subset)
    • NMF derives fewer new “features” taking into account interactions among
    original attributes
    • Supports text mining, life sciences and marketing applications
    Non-negative Matrix Factorization - NMF
    Copyright © 2022, Oracle and/or its affiliates
    10
    

    View Slide

11. Two options to access the Template Examples
    Copyright © 2022, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
    11
    

    View Slide

12. There are Several Feature Extraction demos specific to certain algorithms
    Attribute Importance using MDL Attribute Importance using EM
    Type "attr"
    

    View Slide

13. Dimensionality Reduction using SVD
    Type "dim"
    There are Several Feature Extraction demos specific to certain algorithms
    Dimensionality Reduction using NMF
    Dimensionality Reduction using SVD
    

    View Slide

14. Feature Extraction using ESA
    Type "extr"
    There are Several Feature Extraction demos specific to certain algorithms
    Feature Extraction using ESA Wikipedia model
    

    View Slide

15. Click Create Notebook to create a copy for yourself
    Copyright © 2022, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
    15
    Click on "Create
    Notebook"
    Give it a name
    (and optionally
    a description)
    Click OK
    

    View Slide

16. The new notebook will show up in the notebooks listing.
    Copyright © 2022, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
    16
    

    View Slide

17. Q & A
    Copyright © 2022, Oracle and/or its affiliates
    17
    

    View Slide

18. Copyright © 2022, Oracle and/or its affiliates.
    18
    Thank you
    

    View Slide