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Machine Learning & Artificial Intelligence · Data Mining & Feature Engineering

Data Mining: Supervised Learning (Flexible Models) — Advanced Expert Reference

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Advanced Data Mining

Overview

Topics covered

  • Introduction
  • Flexible Supervised Learning Models
  • The Paradigm of Flexibility and Bias-Variance Commitment
  • Theoretical Decomposition of the Mean Square Error (MSE)
  • Effective Degrees of Freedom ( )
  • Robust Loss Functions

Related concepts

Main connections

  • Decision trees, Random Forests & XGBoost
  • Variance & standard deviation
  • Supervised machine learning
  • Regularization (Lasso / Ridge / Elastic Net)
  • Residual analysis & diagnostics
  • Calculus & differentiation

Compatible notation

Mathematical sample in LaTeX

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

E[(Y−f^(x0))2]=Bias[f^(x0)]2+Var(f^(x0))+Var(ϵ)
View LaTeX sourceE\left[\left(Y - \hat{f}(x_0)\right)^2\right] = \text{Bias}\left[\hat{f}(x_0)\right]^2 + \text{Var}\left(\hat{f}(x_0)\right) + \text{Var}(\epsilon)