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Heuristics Data Science Life Cycle
- 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