Getting closer with Modelling Algorithms

1. Building the
Vision
Together

2. Explainable AI – Frameworks – Classic ML
XAI – Frameworks for Data Science
Pre-Modelling
Unsupervised
/Supervised
Recommend
Feature Engineering
Modelling
Cross Validation
Entropy Aware Sampling
Post Modelling
Heuristic Deviations
Unseen Samples

3. Data Science Life Cycle
1- Scale Wide Data
Explicit Feature
Selection
-Dimensionality
Reduction
Methods
2- Modelled Data
Implicit Feature
Selection
- Modelling
/Segmentation
Algorithms
3 – Un Checked Bounds
Unbounded at
Leaf Nodes
Un Checked
attributes -
greedy attribute
split
4- Un Processed Dimensions
Features Left in
Steps 1 ~3
5 - Unprocessed
Samples
Left by
Modelling
Algorithms to
Reduce Variance
Extract Per Class Feature Heuristics
Automate Bound Checks
Automate - Confidence Score Generation
, Bound Check Violations
Configurable Bound Checks
Index

4. Window Opportunity - GOALS
Interpretable?
Integratable for Digital System?
Room for Deviation Recognition ?
Human Augmentation and Policy Formulation?

5. Insights from Decision Trees Segmentation
1) Generalization
2) Neighbor based Confidence
3) Frequency Segments relation
4) Segment Validation

6. Insights from SVM
1) Layer Insights
2) Angle, Relation

7. Insights from Origin of Neighbors
1) Proximity Tracing
2) Scoring and Performance
Monitoring

8. Convergence for Augmentation Interpretation
Deep Learning
• Data Signature
Modelling
Classical
Machine
Learning
• Dataset
Understanding
Statistics
• Data Space
Understanding

9. Building the
Vision
Together