Professional Development · IT & Digital · Module 2: Regression and Tree Models

Logistic Regression: Classification Fundamentals

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Understand logistic regression — the foundational classification algorithm — how it extends linear regression to probability output, and how it learns decision boundaries.

Inhaltsübersicht

  • From Linear Regression to Probability: The Sigmoid Function
  • Learning: Binary Cross-Entropy, Gradient Descent, and the Decision Boundary
  • Multi-Class, Regularisation, and Probability Calibration
  • Logistic Regression in scikit-learn and Its Place in the ML Landscape
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Learn Logistic Regression: Classification Fundamentals — Machine Learning — From Theory to Practice IT & Digital | Summary, Flashcards & Quiz