BestHub
search
Sign in
Tag

linear models

1 views collected around this technical thread.

Model Perspective
Model Perspective
Jan 7, 2023 · Artificial Intelligence

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

An extensive overview of supervised learning introduces key concepts, model types, loss functions, optimization methods, linear and generalized linear models, support vector machines, generative approaches, tree and ensemble techniques, as well as foundational learning theory, providing a comprehensive foundation for AI practitioners.

AIGenerative ModelsSVM
0 likes · 9 min read
Mastering Supervised Learning: From Linear Models to SVMs and Beyond
Model Perspective
Model Perspective
Dec 1, 2022 · Fundamentals

Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code

This article explains the principles of one‑way, two‑way and multi‑factor ANOVA, demonstrates how to perform them in R using aov(), lm() and anova(), and shows post‑hoc comparisons with TukeyHSD as well as correlation testing with cor() and cor.test().

ANOVARTukey test
0 likes · 10 min read
Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code
Model Perspective
Model Perspective
Oct 24, 2022 · Artificial Intelligence

Understanding RuleFit: Combining Tree Rules with Linear Models for Interpretable AI

RuleFit, introduced by Friedman and Popescu in 2008, integrates decision‑tree‑derived rules with linear regression to boost predictive accuracy while maintaining strong interpretability, and this article explains its definition, rule extraction, algorithmic implementation, code example, advantages, limitations, and practical insights.

RuleFitensemble treesfeature engineering
0 likes · 10 min read
Understanding RuleFit: Combining Tree Rules with Linear Models for Interpretable AI
Model Perspective
Model Perspective
Jul 22, 2022 · Artificial Intelligence

Understanding Ridge Regression: Definitions, Properties, and Parameter Selection

This article explains ridge regression by defining the estimator, outlining its key properties, discussing methods for choosing the ridge parameter, and demonstrating its application to economic data with Python code and visualizations.

Pythonlinear modelsparameter selection
0 likes · 6 min read
Understanding Ridge Regression: Definitions, Properties, and Parameter Selection
DataFunTalk
DataFunTalk
Dec 25, 2021 · Artificial Intelligence

Optimizing Spark‑ML Linear Models with Project Matrix: Background, Progress, and Future Plans

This article introduces the Project Matrix initiative that re‑examines and restructures Spark‑ML linear models, detailing the background of Spark‑ML usage at JD, the performance‑focused optimizations such as blockification and virtual centering, and outlines upcoming work to further improve scalability and accuracy.

Sparkbig datalinear models
0 likes · 9 min read
Optimizing Spark‑ML Linear Models with Project Matrix: Background, Progress, and Future Plans