- Published on
Extending Pyspark's MLlib native feature selection function by using a feature importance score generated from a machine learning model and extracting the variables that are plausibly the most important
All Posts
All Posts
- notes (27)
- r (19)
- singapore (10)
- visualisation (10)
- musings (8)
- thesis-thursday (7)
- regression (7)
- python (7)
- networks (7)
- ols (6)
- spatial (5)
- personal (4)
- graph-theory (4)
- javascript (4)
- data-science (4)
- web-scraping (3)
- sg-economy (3)
- dashboard (3)
- crypto (3)
- benchmarks (3)
- julia (3)
- sg-social (2)
- spark (2)
- big-data (2)
- gcp (2)
- next-js (2)
- mdx (2)
- markdown (2)
- fairness (2)
- ethics (2)
- veritas (2)
- responsible-ai (2)
- sqlite (2)
- wasm (2)
- llm (2)
- generative-ai (2)
- binscatter (1)
- stata (1)
- simulation (1)
- metrics (1)
- blogdown (1)
- dags (1)
- causal-inference (1)
- react (1)
- visualization (1)
- serverless (1)
- learning-julia (1)
- agent-based-models (1)
- tailwind (1)
- guide (1)
- privacy (1)
- marketing (1)
- defi (1)
- amm (1)
- philosophy (1)
- open-source (1)
- duckdb (1)
- citations (1)
- Published on
- Published on
Custom cross-validation class written in PySpark with support for user-defined category such as by time, geographical or consumer segments.- Published on
Simple guide to convert jupyter notebooks to markdown posts which can be published in your favourite static site generator- Published on
Deriving the OLS formula as a means of approximating the conditional expectation function