Decision Trees Unveiled: From ID3 to CART to Random Forests to XGBoost towardsai.net/p/artificial...

Decision Trees Unveiled: From ID3 to CART to Random Forests to XGBoost | Towards AI

Author(s): Joseph Robinson, Ph.D. Originally published on Towards AI. A Comprehensive AI Guide All Machine Learning Engineers and Data Scientists Should Rea ...

Grading machine learning projects. Love to see 𝘣𝘦𝘴𝘵 𝘴𝘶𝘣𝘴𝘦𝘵 𝘴𝘦𝘭𝘦𝘤𝘵𝘪𝘰𝘯 beat out fully tuned KNN, Lasso, random forest, and xgboost models (yes, on test set). #stats

Sure, this (xgboost with forests) is *purely exploratory*. I picked up on it nonetheless because the UK is so weird compared to the US/rest of Europe where radical right party support goes up with age *then comes down*.

Recently forced myself to learn some ML after failing multiple times, and it turned out far simpler than I thought I've only ventured as far as classifying tabular data (with XGBoost), but mainly it feels like putting in a bunch of my own heuristics and the model "learning" the useful combos

I think I was trying to understand XGBoost by reading the src? github.com/dmlc/xgboost Not sure how good of learning resource it is.

Optimist: the cup is half full Pessimist: the cup is half empty Data Scientist: I know what the marketing material says, but an LLM is wrong for predicting the cup capacity and we should just use XGBoost

You very rarely need anything more computationally complex than xgboost in the physical sciences. If you can't train a decent (cf. final) model on your personal laptop you likely should spend more time on understanding the problem, not throw more compute at it.

An XGBoost model identifies children at risk of autism before age 2, 6 months earlier than existing evals

Machine Learning Prediction of Autism Spectrum Disorder

This diagnostic study investigates whether machine learning models incorporating easily obtainable measures from medical records and background history

頂刊APP部署復現——基於XGBoost模型的心臟病風險預測與解釋：Streamlit應用程序開發 #復現#心臟病#應用

頂刊APP部署復現——基於XGBoost模型的心臟病風險預測與解釋：Streamlit應用程序開發

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Hydrology Paper of the Day @B_Ghanbarian suggested by @MLEarthSciences on how training data heterogeneity is important for machine learning models of soil saturated hydraulic conductivity: application of the XGBoost algorithm; learning curves; sample size; and feature importance.

Representative Sample Size for Estimating Saturated Hydraulic Conductivity via Machine Learning: A Proof‐Of‐Concept Study

Learning curves were applied to address effects of data heterogeneity and number of samples on machine learning-based model estimations Concept of representative elementary volume was used to det...