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Machine Learning & Artificial Intelligence · Machine Learning & Ensembles

Validation, Model Selection, and Practical Aspects in Machine Learning

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Validation and Inference in Machine Learning

Overview

Topics covered

  • Introduction
  • Machine Learning: Validation, Model Selection and Practice
  • Statistical Learning Theory and Generalization
  • 1.1 Theoretical Risk versus Empirical Risk
  • 1.2 Rademacher Complexity and Generalization Bound
  • The Bias-Variance Dilemma (Bias-Variance Tradeoff)

Related concepts

Main connections

  • Cross-validation & train/test splits
  • Overfitting & cross-validation
  • Variance & standard deviation
  • Diagnostic test evaluation & ROC
  • Preprocessing pipelines & transform recipes
  • Mean & expected value

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

R(h)=𝔼(x,y)∼𝒟[L(h(x),y)]=∫𝒳×𝒴L(h(x),y)dℙ(x,y)
View LaTeX sourceR(h) = \mathbb{E}_{(x, y) \sim \mathcal{D}} [L(h(x), y)] = \int_{\mathcal{X} \times \mathcal{Y}} L(h(x), y) \, d\mathbb{P}(x, y)