The document discusses predictive modeling in data science, focusing on explicit and implicit feature selection and the various challenges faced, such as unchecked bounds and unprocessed dimensions. It emphasizes the need for automation in bound checks and confidence score generation. Additionally, it references a PhD thesis or book related to feature selection problems in high dimensional data.

1. Predictive Modelling
Preserving Data Heuristics
Present for Solutioning

2. 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

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. References – PHD Thesis / Book
• Feature Selection Problem • Feature Selection for High
Dimensional Data