Interpretable Machine Learning Applications: Part 1

Instructor: Epaminondas Kapetanios

What you'll learn

  •   How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).
  •   How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.
  •   How to get an insight into the way values of the most important features impact the predictions made by the classifiers.

    • Skills you'll practice

  •   Data Import/Export
  •   Machine Learning
  •   Data-Driven Decision-Making
  •   Decision Tree Learning
  •   Random Forest Algorithm
  •   Regression Analysis
  •   Data Analysis
  •   Classification And Regression Tree (CART)
  •   Feature Engineering
  •   Applied Machine Learning
  •   Predictive Modeling

    • Interpretable Machine Learning Applications: Part 2

    Interpretable machine learning applications: Part 3

    Interpretable Machine Learning Applications: Part 4

    Interpretable machine learning applications: Part 5

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